IDEA MARKETS IN THE INNOVATION ENGINE

Master’s !esis for M.Sc. Strategic Market Creation, Copenhagen Business School

Kristo"er !omas Hartwig -

Dept. Marketing - CBS

HANDED IN OCTOBER 6. 2009

TABLE OF CONTENTS

ENGLISH ABSTRACT! 4

Introduction! 5

Background! 6

Research purpose! 7

Problem identification! 8

Three propositions about idea markets! 9

Problem statement! 9

Theoretical approach! 10

Methodological OUTLINE! 11

Delimitations! 12

Duration of study! 12

Location and Culture! 12

Tool! 12

Users! 13

Structure! 13

Enterprise 2.0 ! 14

Prediction Markets! 16

Political and News Prediction Markets! 16

Forecasting Prediction Markets! 17

Preference Markets! 17

Idea Markets! 18

Introduction to the Idea Market: Idea Exchange! 19

The Idea Market tool : Ideas as Social Objects! 21

Knowledge creation! 23

SECI! 24

Espistemology - Tacit and Explicit knowledge! 24

Ontology - Knowledge in organizational domains! 24

Ba - The context and shared space of knowledge creation! 25

Originating Ba:! 25

Dialoguing Ba:! 25

Systematizing Ba:! 25

The Exercising Ba:! 26

Critique of the SECI framework! 28

TYPES OF INNOVATION! 28

Employee Driven Innovation! 31

MANAGEMENT OF INNOVATION! 32

INNOVATION OF MANAGEMENT! 33

TYPES OF Reasoning in innovation management! 34

Abduction and intuition in innovation management! 34

Creativity in collaborative systems FOR INNOVATION! 35

Non-preconception principle! 35

Skunk Works/Heterarchy! 35

Heterarchy! 36

Serendipity! 36

Diverse Stimuli! 36

Internal Communication! 37

Reciprocity! 37

Motivation! 37

Rich Information Provision! 38

Collective intelligence! 39

Types of collective intelligence! 41

CORRESPONDENCE BETWEEN Ba and Collective Intelligence! 42

WISE CROWDS! 43

The Market as an Aggregation Tool for Collective Intelligence! 44

The Case Against Markets! 46

Psychology and Rationality in Markets! 47

Endogenous Markets! 47

Feedback mechanisms! 48

CHAPTER 4"- METHODOLOGY! 48

Research Preperation:! 49

Sample! 49

Research Design! 50

Research Paradigms: Qualitative and Quantitative research! 50

Mixed methods research! 51

The choice of research methods in idea markets.! 51

Research Strategy! 52

Data collection : Primary Data! 54

Secondary Literature! 56

The Three Cases! 57

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CHAPTER 5: Analysis! 59

The Three Cases! 59

Case 1: Multinational Chemical Co. - Case of a Beauty Contest?! 59

Introduction:! 59

Satisfaction! 64

Another type of collective intelligence! 64

Case 2 : Multinational Pump Co. - Collective Intelligence?! 65

The Idea! 66

Socialization! 66

Externalization! 67

Combination! 67

Internalization! 67

CASE 3 : PHARMACEUTICAL CO - The Idea Markets as Ba! 70

Originating Ba, Socialization! 71

Interacting Ba & Cyber Ba, Externalization and Combination.! 72

CHAPTER 6 : Perpectives! 78

Limitations of the Study and Suggestions for Further Research! 80

A good idea market and a good Ba! 81

CHAPTER 7 - CONCLUSION! 81

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ENGLISH ABSTRACTThe use of ‘idea markets’ refers to the practice of combining an online suggestion-box with idea-evaluation by the use of a virtual stock market. Here, the ideas and suggestions are floated on a internal stock market, within an organization or another group of stakeholders. All the participants in the stock market are handed an amount of play money, which they can use to invest in the ideas and suggestions in the market. The share price in ideas are determined by the demand. Therefore, the more people to invest in an idea, the higher the share price. This means that participants who spot a popular idea early will stand to make a profit on his or her investment, an incentive to keep evaluating new ideas as they are shared. The question posed in this thesis is how such markets can impact innovation in companies.

A possible structural impediment to idea markets is the market mechanism. While market mechanism is efficient for aggregating dispersed knowledge and information, it might not be effective if participants show speculator behavior such as described in Keynesian Beauty Contests (Keynes , 1936). Surprisingly, it is found that a minority of participants following the speculator type behavior may help market efficiency without compromising the evaluation of quality of ideas.

Idea markets are claimed to represent a method for aggregating the collective intelligence and ‘wisdom of crowds’ from large groups of people. Therefore the concept of collective intelligence is explored in theory and in practice in idea markets. By opening up to the philosophically rooted knowledge management of Ba, idea markets are observed to have commensurate qualities with some types of collective intelligence.

A final and more pragmatic proposition that is explored, is whether or not idea markets act as a catalyst for creativity, knowledge sharing - in other words innovation in companies. Idea markets are evaluated on the basis of a check-list comprising of 8 factors that stimulate creativity in intranets in organizations, and is found to affirm those factors.

Based on the findings, the outlines for a new model for innovation management is proposed. The model encompasses the use of crowd sourcing and abductive thinking, and follows Gary Hamel’s notion of passion as an important principle for innovation management. It is suggested that a balanced idea market can act as a metric for said passion.

Crowdsourcing by the means of an idea market can help lessen the importance of positivistic and indictive justi"cation of idea processing, leading to a more intuitive method where tacit knowledge can #ow into the decision making in innovation management thus paving the way for abductive reasoning which is thought to lead to more radical and disruptive innovation. At the same time, the market as an aggregation mechanism can lead more e$cient knowledge management and decentralized innovation in and around organizations.

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“All markets are conversations”(!esis 1: !e Cluetrain Manfesto, 1999)

"E$ciency is doing things right; e%ectiveness is doing the right things.”

(Peter F. Drucker, American Educator and Writer)

CHAPTER ONE

INTRODUCTION

Imagine this: You’re an employee at a relatively well-established corporation. Here, there are routines for most of the actions you carry out during your daily work. For example, if you have an idea for an improvement in your department’s processes or great new product, there is a suggestion box at the end of the hall for you to submit your ideas. Just as if you were writing a letter to someone, the idea travels silently from the sender to the intended receiver. &e letter is opened and processed, presumably by an innovation-o$cer or someone from HR at your company. &at person now designates where the idea is going. She may send it to a review-board, she may scrap it, she may send to another unit in the organisation. Who knows? Chances are you’ll get an email thanking you for sharing your thoughts, and that will be the last of it.

Now, imagine that instead of writing down and sending your proposed idea, you stand up in your open o$ce space, grab a megaphone – and loudly interrupt everyone with your idea. Everyone hears it. Someone else, at the other end of the o$ce may grab another megaphone and reply with comments, criticism or praise for your idea. But most people will probably tell you to quiet down and stop interrupting them any further. Your chances of winning a popularity contest at the o$ce are thoroughly diminished.

&ese two methods of conveying ideas have their bene"ts - and I imagine you will agree, their drawbacks too. &e former is signi"ed by certain degree of sequential order. &e latter lets you broadcast your idea, but it would most likely lead to chaos and loss of popularity in the o$ce space. Sitting somewhere between these two procedures, is the notion of idea markets, which is the subject of this master’s thesis. &e act of balancing order and chaos is an underlying theme of many innovation-related challenges, also with regards to idea markets. Furthermore, an idea market could be just as susceptible to e%ects such bubbles and greed that real life markets are. On the other hand,

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the idea market may more akin to a market square than a stock market. &e thesis will describe, observe and deduce principles about the practice of setting up an internal market for suggestions aimed at innovation in companies. It aims to understand the use of such a market through perspective of innovation principles and theory of knowledge management.

BACKGROUND

What are idea markets? In idea markets, you mix on-line suggestion boxes and virtual stock markets, for proposing ideas in a larger forum, set up so that members evaluate and develop everyone else’s ideas collaboratively.

One of the earlier examples of a market mechanism and stock market being used in an organisation was 1996 when HP’s implementation of a prediction market, known as BRAIN (Kambil 2003, Chen, Fine & Huberman 2003). Here, HP employees were equipped with an account of play money, and asked to wager on forecasting issues. In BRAIN, employees could buy futures in expected demand for computer memory. &e argument went, that the people who were in contact with customers would be able to make a more informed estimation of such "gures than the members of top-management. &us, the employees could supply top-management with forecasts based on the direct knowledge and experiences from the market. Eli Lily began using prediction markets for industry related futures in 2003 and the following year, they began using them inside the corporation (Masse, 2007).

A year later, the market mechanism was applied to idea evaluation, the case of Canadian company Rite Solutions. A journalist from &e New York Times wrote an article on the company, and the article serves as a good starting point, because this was the article that led me, as student, on to idea markets:

“an internal market where any employee can propose that the company acquire a new technology, enter a new business or make an e!ciency impro"ement. #ese proposals become stocks, complete with ticker symbols, discussion lists and e-mail alerts. Employees buy or sell the stocks, and prices change to re$ect the sentiments of the company's engineers, computer scientists and project managers — as well as its marketers, accountants and even the receptionist.” (Taylor, 2006).

In Taylor’s 2006 article, the leaders at Rite Solutions had noticed how the distributed innovation process had lead the company in new directions, coming up with the new product line “Rite View”, which accounted for 30% of the company’s revenues in the following year.

Another company that has been using the idea market method is US based 'ualcomm. Here, the markets have been used in the annual Venture Fest program, where employees are invited to share their best ideas. &e winning ideas, those that are the subject to the most invested ‘playmoney’ are turned into mock companies within 'ualcomm and receive backing and funding. During the initial research for this thesis, Ricardo dos Santos, the Director of Business Development at 'ualcomm has been kind enough to answer me in an online posting about the subject of idea markets.

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“A PM or other collective intelligence approach to the "ont end of inno#ation (creation/selection) is game changing and has several advantages, most notably being able to handle a broad scope of topics and high number of participants (without collective intelligence, and if dependent on traditional committees for decision making, you have to compromise either broad scope or high number of participants ”

(Taken from an on-line posting in an Internet Newsgroup1 between the author of this thesis and Mr. dos Santos )

RESEARCH PURPOSE

&e application of idea markets is a relatively unexplored subject. At the same time, some of the initial reports on the subject, as can be read above, indicate that it can be an area of great promise. Potentially, idea markets may yield unprecedented knowledge coordination in organizations, when it comes to the front-end of innovation. For the scholar of innovation processes and knowledge creation, this thesis will o%er a theoretical account of how idea markets may harness the potential of collective intelligence on knowledge creation processes taking place in the front-end of innovation.

As stated in the introduction, the use of idea markets is understood to stand somewhere between chaos and order. One might say that the market organizes chaos in an orderly manner. Any method that embodies the notion of “Strategy at the Edge of Chaos” ( Beinhocker, 1997) will garner attention from innovation strategists and managers. For it is thought to be the balancing act of ‘chaos and order’ / ‘creativity and control’ that leads to the desired results and successes (Spender 1994, Amabile 1994).

On the face of it, idea markets are not without their pitfalls either. Is the competitive environment of a stock market conducive to creativity and collaboration? What types of behavior are rewarded in such markets? Is it serious business or is it fun and play? &e purpose of this study is provide insight into the use of idea markets in the context of innovation.

&e research problem has been identi"ed as “ How can idea markets, as a mechanism for utilizing distributed intelligence, have an impact on innovation in business organizations, and what are the limitations of this approach?”. &e problem statement is underpinned by the three propositions that have made themselves apparent based in the initial desk research: a) &e market mechanism will turn the market into a Keynesian Beauty Contest, undermining the bene"ts of distributed intelligence in the idea sharing and evaluation phase of the innovation process. b:) &e process in idea markets constitutes the application of collective intelligence to innovation and "nally, c: ) Idea markets are suited as an innovation management tool.

71 “The Role of Prediction Markets in the Innovation Engine” in Google Groups PMIA Group

PROBLEM IDENTIFICATION

&e chosen problem area herein is the application of prediction markets to innovation, more speci"cally the generation and evaluation of ideas and communication of the feasibility of ideas, the front end of innovation. &e literature on the use of virtual markets and the general attitude towards them has had to be examined in order to get a general indication where problems may be de"ned in this "eld. Due to the novelty of the "eld, a majority of the available sources on the subject are currently found in blogs and articles scattered around the web. Some of the questions are :

• How can we best describe prediction markets, especially the variation hereof, called Idea Markets, in relation to the knowledge intensive, networked organization? (T. Davenport’s Blog: Some Predictions on Prediction Markets)

• What implications may this have on hierarchical structures of organizations and culture? ( J. Sviokoa: Bureaucracy Termites: Blogs, Wiki’s and Information Markets)

• What role will managers play in a future of collaborative, social innovation-tools that circumvent current organizational structures? (A. McAfee: Enterprise 2.0, &e Dawn of Emergent Collaboration)

• Why is it that so few businesses use prediction markets? (T. Cowen, “Why don’t more businesses use prediction markets?”)

• Does the use of idea markets in the ideation process constitute collective intelligence? (Idea market vendors are quick to throw around the term ‘collective intelligence’ but what does it really mean?)

• Finally, the mixing of a competitive market which is thought to be driven on the self-interest of agents and seeking collaboration in ideation and idea re"nement. How do these two mix? Can there be an ‘Invisible Hand’ governing the ideation process?

&e articles on prediction markets are generally indicative how new the "eld is, the possible future of it and how prediction markets may play a role in handling some of the challenges that organizations face in adapting to an interlinked, accelerated economy that spans large parts of the globe. Furthermore, a number of companies has let it be known that they have been conducting experiments with prediction markets internally. Eli Lily, Shell, Google, Hewlett Packard and Microso( have been using prediction markets for both forecasting and innovation e%orts.

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THREE PROPOSITIONS ABOUT IDEA MARKETS

&e three issues have made themselves sequentially apparent as the work going in to this thesis has progressed, and they serve as the basis for formulation of the three propositions. Firstly, the most characteristic part of idea markets may be the markets themselves. &e market mechanism will inherently reward those able to predict what others perceive as valuable. If you are able to predict what the average stock buyer will "nd attractive, you can speculate and make a pro"t. Of course, one could gain even more from speculating what such a speculator is investing in. &is type of behavior is seen in a Keynesian Beauty Contest (Keynes, 1936). Just as this is a threat when valuing stock, the same e%ect can be detrimental towards the collective valuing of ideas shared in idea markets. &erefore, it is found to be essential for the understanding of idea markets for innovation, and must be explored.

Secondly, should the market ‘work’ for proposing and evaluating ideas, is it a representation of collective intelligence? Academics (Malone, MIT Center for Collective Intelligence, 2006) and private companies (Inkling, Qmarkets, Spigit) alike claim that virtual markets can be utilized to harness the collective intelligence of large groups of people. Does this hold true for idea markets? &ere are many de"nitions of what collective intelligence constitutes, and the answer may not be a straight-forward yes or no.

Finally, the work and research with prediction markets during the work with this thesis has lead to actual application of prediction markets, which is the basis of the third proposition. &e proposition is that idea markets are bene"cial to innovation in companies. Principles on creativity in intranets have been proposed by di%erent writers, such as Stenmark (2006) and Robinson & Stern (1997). &ese principles are applied to the "ndings made on idea markets.

PROBLEM STATEMENT

From the initiation of this thesis the aim has been to explore the relation between innovation (as strategy and process) and idea markets.

“How can idea markets, as a mechanism for utilizing distributed intelligence, have an impact on innovation in business organizations, and what are the limitations of this approach?”

&e formulation of the statement makes it possible to integrate or synthesize two available "elds – innovation and idea markets- the former which is accepted as one of the most important aspects of strategy, with the latter, a "eld that has been noted to hold exciting potential with regards to information aggregation for decision making or support.

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&e question has become the basis for the three propositions that will be explored in the present thesis:

P1: The market aspect of idea markets can prove detrimental to the overall purpose of idea markets.

P2: Idea markets are an actual representation of collective intelligence.

P3: Idea markets are well suited as an idea management tool and has a positive effect on innovation.

THEORETICAL APPROACH

To frame the relatively unexplored "eld of idea markets in relation to pro"t seeking organizations, a number of sources on the subjects of knowledge management and innovation are reviewed. Strategically, the implied goal of implementing an idea market is to have large groups people contribute with, and contribute (Surowiecki, 2006) to ideas that can provide the company with a competitive edge built on ability to operationalize information, knowledge and creativity as the company adapts to it surroundings - in other words - dynamic capabilities. Davenport & Prusak (1993) denote knowledge as the most important source of a sustainable competitive advantage in what has been called the Innovation Economy. A rather new principle for managing and developing knowledge has been the notion of Crowdsourcing (Howe 2006, 2008), wherein an architecture of participation is set up, e%ectively letting everyone share their particular knowledge in di%erent e%orts - the most notable example being Wikipedia.

Nonaka, Takeuchi and Konno’s theory of knowledge creation is used to highlight the processes and the context of the idea markets. Another key-aspect of the idea market is the application of the market mechanism. Di%ering theories of market and market behavior are examined. In relation hereto, it is observed what the competition in a marketplace can mean for knowledge dissemination and innovation. F.A. Hayek’s theory of markets with free pricing mechanisms (Hayek, 1945) and the Keynesian “Beauty Contest” concept (Keynes, 1936) are used to this end. &e former bears a promise that markets may be an e%ective tool in innovation in companies. &e latter suggests that the market mechanism will drive people to make uninformed decisions in the market, making the collective appraisal of the ideas less valuable. Futhermore, economists such as Lars Tvede (1993) and Robert J. Shiller (2000) note that psychology plays an important role in stock markets. To some degree, these thoughts are applicable to idea markets also.

Aside from academia, a number of sources occupied with the concepts and uses of new networked digital enterprise-tools will be reviewed. &e current use of prediction markets in di%erent business areas is o(en ascribed to the success of James Surowecki’s bestseller “&e Wisdom of Crowds: Why the Many Are Smarter &an the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations” from 2004. Surowiecki describes a number of cases of crowd intellect, and deduces a set of principles on how this ‘crowd knowledge’ can be harnessed to di%erent purposes, where predictions or answers are required. As shall be seen, many of the

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prediction market vendors are using Surowecki’s thoughts – furthered by notions of collective intelligence and the e%ects digital social networks inside companies.

METHODOLOGICAL OUTLINE

To gain a more concrete picture of how idea markets function and impact organizations in practice, the virtual market specialists Nosco have been followed through three cases of implemented idea markets in three di%erent Danish companies during a 6 month period. For research purposes, an online tool like Nosco’s idea markets makes it possible to draw data from the individual users. &is provides access to an array of quantitative data. In the three implementations, all users were invited to respond to a survey about their behavior in idea markets and their general perception of idea markets in their work-lives. Furthermore, interviews were carried out with two selected participants in idea markets. &is combined with the researchers’ everyday interaction with the idea markets provides the means for the qualitative data in the thesis.

In this thesis, the concept of idea as social objects should be seen in relation to understanding idea markets as innovation tools in companies. !e problem statement; “How can idea markets, as a mechanism for utilizing distributed intelligence, have an impact on inno#ation in business organizations, and what are the limitations of this approach?” has been broken down to three propositions that are investigated on a case by case basis.

P1: !e market aspect of idea markets can pro#e detrimental to the overall purpose of idea markets.

!e unique feature the idea market tool is the market mechanism. On one hand, it serves as an aggregation mechanism for large groups deliberating about ideas for innovation. However, this feature also entails that an element on competition is introduced. !e answer to whether the element of competition can be detrimental to the overall evaluation of ideas, is sought in the behavior of the participants in the idea markets. !erefore, participants are questioned about their approach to the ideas and the competition. the answers are compared to known theories about markets, rationality and behavior.

P2: Idea markets are an actual representation of collective intelligence.

!e market mechanism is the aggregation mechanism for all the views and judgements for the participating crowd in idea markets. To examine the second proposition, di"erent concepts of collective intelligence are presented and compared to the process of knowledge creation in idea markets. Ideas are understood as social objects, that move through a knowledge creation process, which is known as the SECI model. !e SECI model was originally devised by Ikujiro Nonaka and his co-authors from the Japanese Advanced Institute of Science & Technology. A move towards new collaboration methods with collective intelligence implies normative changes in the knowledge creation process. By juxtaposing the SECI model with the participants’ experiences of process in idea

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markets, the second proposition is investigated.

P3: Idea markets are well suited as an idea management tool and has a positive e$ect on inno#ation.

!e third proposition is explored to give an overall picture of the impact on innovation and creativity. Using Nonaka & Konno’s elaboration on the SECI model - !e concept of Ba - idea markets are sought to understood in the context of a knowledge work environment. !e proposition is also examined by going through 8 factors that are thought to be conducive for innovation in intranets in workspaces (Stenmark, 2006) and comparing with the participants’ experiences with context of the participation in idea markets.

DELIMITATIONS

As a means of delimitation, this thesis revolves around a focal point; product innovation and the associated procedures and strategic choices. &is is re#ected the methodical choice of linking case-studies (procedures) with a systemic view of the organisation and its environment (strategic choices).

&ere are a number of delimitations that should be observed, with regards to the study:

Duration of study

&e boundaries of the study are "rst and foremost set by the fact that the use of idea markets is such a novel "eld at the time writing. &ere was access to three Danish companies utilizing Nosco’s idea markets for a limited period of time, as they were testing the applicability of the tool. &e longest ‘running’ idea market in the study had a duration of one month. Future studies would ideally include idea markets running continually to better understand the organizational impact of the tool in the daily lives of users. Furthermore, a longer period of usage could enhance users abilities and understanding of idea markets, just as the possible novelty of such a market could well wear o%.

Location and Culture

All three cases of idea markets that have been studied are Danish companies, two of which included employees situated abroad. Writers such as Geert Hofstede (1984) suggest that cultural di%erences can lead to great variance in social interactions in organizations. Hofstede’s proposed factors such as power-distance and individualism could be deciding for the idea markets, as users participate in social interactions. It should be mentioned that Nosco’s Idea Exchange platform allows for users to be anonymous when sharing suggestions or comments.

Tool

While the study seeks to gain insights regarding the use of idea markets in general, only one type has been used, Nosco’s Idea Exchange. &ere are a number of idea market tools available with slightly di%erent features. Examples

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include Idea Exchange’s feature for anonymity and that users cannot invest in their own ideas. Ottaviani (2008) mentions such a feature as making the market less susceptible to manipulation. However , principle and mechanism of idea markets are understood to be relatively alike.

Users

Finally it is important to be aware that all the users in the idea market studied are employees of the respective companies. Involving other stakeholders as partners or customers could lead to other sources of innovation. However, both access and the Enterprise 2.0 framing of idea markets in this study means that idea markets aimed at employees are what is referred to with the term ‘idea markets’.

STRUCTURE

Chapter 2 will give a background on idea markets. Firstly, idea markets are the product of a branch of virtual markets namely prediction markets. the history and evolution of prediction markets will be presented. Furthermore, idea markets will be presented on the background of ‘social so(ware’ and its application in the business context, which has been dubbed ‘Enterprise 2.0’ (McAfee, 2006). Enterprise 2.0 and Web 2.0 does not just represent an array of new, high-tech tools, it also represents a new ethos about how people collaborate around the processes that drive innovation. Enterprise 2.0 will also be presented.

Chapter 3 is the literature review. &eories on market behavior, knowledge creation, collective intelligence and innovation are presented, analysed and synthesized.

Chapter 4 gives the methodology and research design.

Chapter 5 presents the analysis. &ree companies that have used Nosco’s idea markets have been studied and will be presented in three cases that are themed to deal with three of the theoretic components; &e "rst case mainly deals with Keynes Beauty Contest and market behavior. In the second case, the development of one idea in the idea market is followed through the lens Nonaka’s knowledge conversion model (SECI) and Engeström’s Social Object principle. In the third case, Nonaka & Konno’s model of knowledge creation environments, also known as Ba, is used to gain an understanding of the organizational impact in the everyday lives of employees at companies using idea markets.

Chapter 6 is a look at the "ndings of the research. Essentially, this chapter will revolve around the third proposition : “Are idea markets an e%ective innovation tool for companies?”. Dick Stenmark (2007) uses a check-list for ‘rating‘ how well intranets can support innovation. &is check-list is applied to the "ndings from the study. Chapter 7 is the conclusion. &e overall problem statement and the three propositions will be reviewed.

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CHAPTER 2

&ere are two fundamental principles tied to idea markets that may well be unconventional to the reader. Enterprise 2.0 and the use of virtual markets such as prediction markets. Enterprise 2.0 represents a new set of tools based on networks that have emerged in recent years, o(en referred to as, Web 2.0. &e concept and the the underlying ethos is presented. It is noted that Enterprise 2.0 is not solely about the instrumental use of tools, but also about context. Next is the use of virtual markets. A history of the application of the market mechanism used to predictive ends is given. Furthermore, the application of virtual markets, prediction markets and idea markets, in the corporation is also given.

ENTERPRISE 2.0

In 2004, tech guru Tim O’Reilly coined the term “Web 2.0” to cover the notion of the new networked and largely social ways that World Wide Web is being put to use in the wake of the dot.com era in the late 90’s. Brie#y put, the ‘2.0’ refers to how the internet has changed from being a source of information, mainly providing access to databases (Information silos) to a socially networked source of sharing and creating on a platform that is built upon the principles of participation and openness. Rather than than using the digital communication platforms to access content, the users are interacting with each other.

While this paper will not occupy itself too much with the technological aspects of Web 2.0, its underlying tenets are of importance as they coincide with the goals and objectives that many contemporary business strategists have been touting in this decade. (Voelpel, Davenport, Chesborough, to name a few) &ere is a clear indication that the new strategic objectives (Innovation, decentralization) are mirrored if not realized in web 2.0 in the way it handles and disseminates knowledge and allows humans to interact within social digital networks. A seminal source on what could be called the 2.0 ethos is Tapscott & Williams’ 2006 book “Wikinomics: How mass collaboration changes everything. &e authors claim that pofound changes will entail the realisation of 2.0:

“deep changes in the structure and modus operandi of the corporation and our economy, based on new competitive principles such as openness, peering, sharing, and acting globally’ (Tapscott & Williams 2006)”

A recurring element in modern business is the focus on the ability to organize and manage in order to handle or harness changes in technology and preferences. In other words, the capability to innovate is crucial when looking at business strategy. In order to delimit the scope of this paper yet make it useful and relevant, innovation has been chosen as the focal point for understanding the implementation and use of idea markets in the corporate setting.

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Innovation seems to be a panacea in relation to business strategy in this decade, rendering the scope of the paper virtually limitless and overly complex. In an attempt to accommodate this, the term “innovation” will be scrutinized in order to discover what parts of the innovation process are impacted by prediction markets.

Web 2.0 applications applied to business purposes have been dubbed ‘Enterprise 2.0’. Enterprise represents more than a set of tools. It can also be understood as an ethos and a school of thought which is outlined in table 1 below. &e core message here includes openness, #exibility and a relaxation of hierarchies and other corporate rigidities. Social so(ware can be described as so(ware systems wherein the many can communicate with the many. Many-to-many is a communication paradigm, which lies in succession of one-to-many, the communication model that the "rst iteration of the world wide web was primarily used for. When communication is based on architecture that allow many-to-many communication, the properties of a network is in place. Whether it is blogs, wikis or prediction markets (McAfee, 2006) a common denominator is the way that the social so(ware acts as a catalyst and platform for networks.

Table 1: The differences between Enterprise 1.0 and 2.0 (source: E 2.0 Conference 2009)

&e reason for presenting the Enterprise 2.0 ethos is that if one accepts it, it sparks a change in the methodological approaches to researching a tool that has the properties of Enterprise 2.0, such as openness, bottom up, social networks and decentralization. &is will be discussed in the chapter in methodology.

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PREDICTION MARKETS

To understand the application of markets in the corporation, prediction markets are a suitable point of departure. Prediction markets are by no means a new thing. In the "(een US presidential elections between 1884 and 1940, presidential prediction markets featured on Wall Street and daily quotes were published on the front page of New York newspapers (Rhode & Strumpf, 2003). Remarkably o(en, the markets got it right – eleven out of "(een. Only once did an underdog on the markets eventually win, as was the case of Woodrow Wilson in 1916.

More recently &e Iowa Electronic Markets where set up, also with the intent of correctly predicting political events. Today Prediction Markets have gained acceptance in American political discourse, owing much to the pending presidential elections.

Political and News Prediction Markets

Prediction markets became digital with the introduction of Iowa Electronic Markets (IEM) at University of Iowa in 1988. IEM presents itself as a forecasting tool and secondly as an incentive mechanism. IEM is used for research purposes. &is type of public prediction market has gained some recognition in the 00’s. Prediction markets such as News Futures, InTrade and Nyhedsspillet (&e News Game in Danish paper Politiken) have grown to some prominence, e.g. in US media during the 2008 presidential election where prediction markets were compared to polls.

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Figure 1: Examples of public prediction markets. News Futures, The News Game and Intrade.

Forecasting Prediction Markets

Prediction markets also made their way into corporations. IT-companies such as HP, Microso(, Siemens (Tapscott, 2006, p260) and Google (Cowgill) deployed internal prediction markets for forecasting. &e events forecasted, have been key performance goals as well as the likelihood of milestones being reached. HP’s prediction market, BRAIN (Behaviorally Robust Aggregation of Information in Networks), was used to predict the volume of sales in the next quarter. (Huberman, Chen, 2001)

Preference Markets

&e next application of the market mechanism is preference markets (Dahan et al. 2007). In preference markets, stocks are based on di%erent permutations of features to a given product, e.g. a mobile phone. It is well known that producing a mobile phone encompasses a number of trade-o%s, such as type of screen, input device, price, quality, appearance, battery and so forth. Typically, the producers and designers of such a phone will employ methods such as focus groups, markets trends and observation to determine what features are more important to potential buyers. Features and tradeo%s may then be used with a tool such as House of 'uality (or 'uality Function Deployment), and decisions are made.

Preference markets represent a way of using the market mechanism for getting large dispersed groups to perform such a task, this is also known as ‘crowdsourcing’. Typically, preference markets are run in sessions of 2 hours, (Dahan, Spann 2007) with large groups of participants. Here, the di%erent choices of stock, based on the di%erent tradeo%s in designing a cell phone, are presented. As participants begin investing the given set of feature that she feels would be the most "tting for her. A market maker determines the value of each stock as the demand rises, just

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as the share price will fall if owners of the stock decide to sell their stock. Of course, there is another strategy to such a challenge: Rather than buying stock in the combination of features you desire the most, you could attempt to predict what the most popular combination of features will be, when the trading session closes. &us those who invest early will gain a pro"t by predicting and investing in, what the traders, as a group, end up buying the most of. &ese two di%ering trading strategies in markets will be treated later, as this lies in the crux of what could be detrimental to idea markets.

Idea Markets

&e mechanism and the behavior that is rewarded in preference markets a much akin to that of the idea market. However, here participants can share their own ideas and let other evaluate them through investing in shares. &e front end of the idea market is where users can post their ideas or suggestions to the themes or problems that are de"ned by the person or group setting up the idea market. An example could be a company asking its employees for new ideas on making processes better or new products that the company should consider making.

&e application of a prediction market to make a crowd assess ideas or suggestions by other members of the crowd is what is referred to as idea markets. &e word ‘idea’ comes from the ancient greek word “idein” which can be understood as ‘to see’. Plato’s description of an idea had mostly to do with the ‘ideal’ or the ‘universal concept’ of any given thing, be it horse, a mountain or a table. In the more modern sense of the word, an idea is basically anything the mind can think, recreate conjure up.

In contemporary management the word ‘idea’ is related to issues such as idea-management, idea banks, idea creation, ideation phases and so on. In the Platonic sense of the word, idea management would refer to managing the concepts that employees cognitively operate with. In the markets that companies such as Rite Solutions, 'ualcomm and Nosco’s clients use (Taylor, 2006), the ideas are understood as suggestions for products, markets and processes.

Market Type Use Examples Mechanism

Prediction Market (Predicting news & event driven derivatives)

Futures trading based on the outcomes that are typically related to the outcomes of public events, such as elections.

Iowa Electronic Market, News Futures, Nyhedsspillet (The News Game), Hub Dub.

Double auction or market maker.

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Market Type Use Examples Mechanism

Prediction Market (Forecasting)

Futures trading based on the outcomes that are typically related to the processes of a given organization: “Will project B be done before December” or “Will our demand for parts rise this summer”

HP, Google,

Market Maker

Preference Market Aggregation of preference from large groups of people, based on short periods of trading. Typically mutually exclusive features can be evaluated with a view to product design.!

Experiments of Dahan et al

Market Maker, calculating the price of each stock in the preference market based on demand.

Idea Market Sharing and evaluation of ideas at the Fuzzy Front End of Innovation.

Platform used in “idea competitions” and a method for electronic brainstorms.

Nosco, SpigitNosco, Spigit"(Providers) Qualcomm, Rite Solutions,

Market Maker, calculating the price of each idea stock, based on demand.

Table 2 : A typology of prediction markets

INTRODUCTION TO THE IDEA MARKET: IDEA EXCHANGE

A practical introduction the tool that the participants in all three cases have used, Nosco’s Idea Exchange is included to give the reader more detailed understanding of the process of an idea market.

&e principle of the idea market is very simple. Invite a group of people (in the three cases : employees) to participate. Each user gets a pro"le and an account of play money (Say, 100,000 )). In the idea market, a set of

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categories can be set up - all ideas shared are to "t into one of these categories. O(en leadership will choose categories that are aligned with corporate strategy. Now, all participants can type in ideas and invest their play money in the others’ ideas. All ideas start with a common share price, say 5 ). &e share price of ideas will rise and fall according to demand. However, the share price cannot fall below the initial valuation.

Figure 2 : Noscoʼs Idea Exchange, the idea market platform used in the thesis

&e two screenshots seen above show a page featuring an idea share and the main page respectively. On the page of the idea share, the users can comment on the given idea, buy shares and sell them if they have any. &e main page shows recently shared ideas, idea trader ranking and pro"le status.

In all the cases of idea markets that have been studied in the research for this paper. &e idea market has been presented to the participants as a competition to "nd the best new ideas and idea traders in an event, a Best Idea Competition, running for 2-4 weeks. If the opportunity has presented itself, a more permanent idea market would have been desirable as a sample. Firstly because there is the risk that the competition-element could e%ect behavior. Secondly, because there may be unforeseeable long term e%ects that will not play out in the short term of the cases.

At all times, the participants have the option for anonymity, when sharing ideas. &e anonymity may be switched on and o% as the participants wish. &is can both help participants that may be too ‘shy’ to share their suggestions or comment. Furthermore, ideas shared by ‘Anonymous’ may be judged on more objective terms than ideas shared by ‘&e Boss’ or ‘&e Janitor’. &e ‘winning’ ideas are those that "nish in the top 3 or 5, subject to the companies’ choices. &e winning traders were the top 3 most pro"table traders in the two "rst cases and the 10 best in the third. &e rewards for best idea and best trader were fairly even in the two "rst cases. In the third case there was more emphasis on the winning ideas, and the top 10 traders all received the same token prize, such as a bottle of wine or ticket to a movie. &e market closing time was not disclosed in any of the competitions, only the date. &is to avoid last-minute speculation.

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THE IDEA MARKET TOOL : IDEAS AS SOCIAL OBJECTS

Ideas, are social in their nature. Looking to the American pragmatists from the beginning of last century, such as James, Pierce and Dewey, Louis Menand (2001) puts it;

“[they] all believed that ideas are not things "out there" waiting to be disco#ered but are tools people in#ent — like knives and forks and microchips — to make their way in the world. !ey thought that ideas are produced not by individuals, but by groups of individuals — that ideas are social.”

I posit that ideas, certainly in the context of suggestions for innovation as products, processes, strategy or otherwise are socially constructed as the the pragmatists de"ne it. Such as it is also de"ned in the epistemology of Nonaka’s SECI model, the chosen theoretical framework for this paper.

Furthering the pragmatists’ de"nition of ideas as socially constructed, I also posit that ideas become social objects in idea markets and various other enterprise 2.0 tools. &us, ideas are not merely constructed via interaction - the users can also interact with the explicit idea. In the di%erent type of ‘2.0’ there are a number of social objects, around which the users organize. To denote what type of interaction is possible with the di%erent social objects, Engeström points to the verbs that follow the di%erent kinds of interactions. On E-bay, you buy, bid or sell. If using social bookmarking services such as Delicious, you can tag and share. With photo and video services such as Flickr or YouTube, you can view, upload, tag and share you pictures and videos. With friends and events as social objects in services such as Facebook, users can add, join, like and discuss with their friends and acquaintances. How so for ideas?

In idea markets, it is ideas that are the social objects that members organize around. &e verbs, signifying the actions that participants can perform are;

Share: &e basis of the idea market is the participants ideas. All users can share as many ideas as they wish. One restriction that might apply is ‘categories’ that only ask for certain types of ideas. When sharing an idea, the participant is prompted with a headline for their idea, a description and the option to share "les such as documents, pictures, video or hyperlinks that are relevant for the communication of their idea. Finally, in Nosco’s idea markets there is the option for anonymity. &is is a function that can subvert some of the social inhibitors that may come with group work. &e user has the option to switch anonymity on and o% as they wish.

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In!est - Support: Once an idea has been shared in the idea market, all participants aside from the author of the idea can invest in the idea. In the three case that will be described, all ideas were #oated on the market with a share price of 5 $. As users invested in an idea, the price of the share would rise, corresponding to the total sum of invested play money in that idea. In the markets, the price elasticity of an idea relates to the total amount of liquidity available in that market. &is means that if all users pooled all their play money in the same idea, the price of that share would be 100.

By investing in an idea, it is also promoted to a higher ranking. &us, investing in an idea can help the idea to more attention.

&us there are four types of interactions made with the social object in the Idea Exchange system. &e ability to buy/sell (promote/demote) ideas is where idea markets make the the most signi"cant departure from others types

Sell - Demote: When users have invested in ideas, they are free to sell their shares. &is means a fall in demand and therefore a reduction in the share price. Typically users will sell if they spot an idea they "nd more worthwhile than the one they have already invested in. As we shall see from the observations in the cases, participants will o(en sell their shares when they feel an idea has become overrated.

Expand - Comment : All users can add to an idea by leaving comments on the individual ideas and attach "les. Seeing as we are looking to harness collective intelligence, this feature is especially important as it opens up for all to share their knowledge and information. In the three instances of idea markets that have been followed in this thesis, the comments fell into three categories: Added knowledge/information, support and criticizing comments. &e last category was not very widespread.

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of groupware for idea generation. &us support for ideas can be given, without having to explicate the reason for supporting it. In this way, tacit knowledge, experienced as intuition, hunches and abductive reasoning can go into the evaluation of the ideas.

CHAPTER 3 - THEORY

KNOWLEDGE CREATION

&e basic premises for the study of idea markets is to understand how they may impact innovation in companies in their e%orts to become more adaptable and pro"table. In the seminal book “&e Knowledge Creating Company: How Japanese Companies Create the Dynamics of Innovation” (Nonaka & Takeuchi 1996), two professors of management from the Hitosubashi University, contended that the reason Japanese "rms were repeatedly beating their American and European counterparts was linked to their ability to be inno#ative, meaning that they created new knowledge and used it to produce successful products as well as technologies. As the global economy has moved from being mainly industrial over knowledge based and towards the innovation economy (Davenport, 2006), the underlying tenet of knowledge creation has become increasingly important.

Nonaka divides the view of knowledge creation in process and context. &e process of knowledge creation is described in the SECI framework, where knowledge conversion between tacit and explicit knowledge take place. For context, Nonaka and his collaborators turn to the ancient Japanese existential concept of Ba . Ba refers to the context, or conditions, where knowledge creation takes place. SECI and Ba are the theoretic components that go in to knowledge creation, giving the company a competitive advantage through innovational capabilities. &e connection is illustrated in the diagram below.

Figure 3: Process and context, Nonaka’s two elements of knowledge creation

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SECI

&e SECI (Socialization, Externalization, Combination and Internalization) process refers to conversion of knowledge types through di%erent domains as new knowledge is created. To better understand the model and how idea markets can play a role in the process, the epistemic and ontological premises of the theory are described.

Espistemology - Tacit and Explicit knowledge

When devising a theory for knowledge creation it is pivotal to understand the underlying theory of what knowledge is, in other words epistemology. Nonaka’s model adopts a distinction between explicit- and tacit knowledge. Explicit knowledge is the positivistic, objective type, that can be codi"ed and transmitted back and forth between you and me without loss of meaning. In large organizations explicit knowledge is easily transferrable from one to the other. In the age of digital tools, even more so. Explicit knowledge refers to the factual, practical side of knowledge.

Nonaka adopts the concept of tacit knowledge from Polanyi, who wrote that “we know more than we can tell” (Polanyi 1967, p. 4), thus adding a subjective dimension to what knowledge is. &is goes for physical dimension where it is well known that activities such as riding a bike or skiing cannot be learned from a book. People need to be guided, taught or a least practice by themselves in some way. But tacit knowledge has another dimension to it. Polanyi’s theory was that even scienti"c knowledge would o(en be the result of a scientist who operated on a ‘hunch’, a creative perhaps inexplicable insight. Polanyi referred to it as “acting on passions” (Ibid). Tacit knowledge is less codi"able and therefore not as easy to communicate and broadcast. However, what Nonaka and Polanyi refers to as tacit knowledge is vital to the creative processes that underpin knowledge creation and thus innovation.

Ontology - Knowledge in organizational domains

Knowledge in organizations is not only transferred between subjective and objective dimensions, it also moves between di%erent domains, the individual, the group, the organisation as a whole and the organizations interactions with its surrounding organizations and the market. Nonaka & Takeuchi (1995) thus present an epistemological and an ontological dimension of knowledge creation, see the illustration below. It could be argued that the "nal dimension, the inter-organizational one, can also be the ‘market’ in which the company "nds itself. &is is the sentiment echoed in Searls, Weinberger et. al’s “&e Cluetrain Manifesto” (1999) and Clay Shirkey’s “Here comes everybody” (2008). Again, the ‘2.0 ethos holds that loosely coupled individuals can now organize around companies and products, thus making it conceivable that users, customers and other stakeholders in the market are an entity in the ontological dimension.

&e knowledge conversion and creation process, takes place through four patterns of interaction in the organisation; Socialization, Combination, Internalization and Externalization. Socialization represents the interaction between individuals through mechanisms such as observation, imitation or apprenticeships. Externalization is the act of converting tacit knowledge into explicit knowledge. Combination involves combining

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explicit knowledge through communication, be it face to face or through digital media, such as idea markets . Internalization is the act of converting explicit knowledge into tacit knowledge.

BA - THE CONTEXT AND SHARED SPACE OF KNOWLEDGE CREATION

Aside from the process in the SECI model, Ba is used to describe and understand the context of knowledge creation. Ba, in the words of Nonaka and Konno, is a “shared space for emerging relationships”(1998, p.40) with the intent of knowledge creation.

Such a shared space of can be physical, virtual, mental or a combination hereof (Nonaka & Konno 1998). &e context, or environment, where innovation takes place has been highlighted as one of the most important catalysts for creative thought and innovation. In “How to Kill creativity” Amabile (1988) pointed to a number of managerial practices that constitute the context of the creative processes in companies that can either make or break knowledge exchange and creativity. Literature on innovation is littered with case and anecdotes (Beassant on 3M, Davenport on Apple) of how companies that create conditions that are conducive to creativity, experimentation are leaders in their respective "elds. &e Ba concept provides a framework and a set of distinctions for addressing the conditions for such knowledge creation. Ba is described through four di%erent notions that are related to the four factors of the SECI model. &e four notions are:

Originating Ba:

Originating Ba is the social space where individuals can share experiences and emotions. Originating Ba corresponds the Socialization phase in the knowledge creating process. In philosophical terms, this Ba can be considered the existential plane, where tacit knowledge can be tranferred between individuals. (Example: Mr. Weinberger notices all the new ‘social’ services online such as blogs, wikis and idea markets - mentally, he groups these, but does not “put it into words” (Foucault: “Mise en Discours”, 1976, Sexulalite) He discusses the grouping with Mr. O’Rielly.)

Dialoguing Ba:

&is corresponds to the Externalization phase of the SECI model. It is a space where tacit knowledge is transferred and made explicit, or formulated. &e key factor is dialogue and creation of mental models that can serve as a common frame of reference. In philosophical terms, the Ba can be considered a re#ective phase where tacit knowledge is made explicit so that groups can interact. (Example: Mr. Weinberger and Mr. O’Rielly come up with a common name for the phenomenon: “Web 2.0”. &e concept is instantly graspable for others, although their understandings of the word will vary according to their speci"c knowledge.)

Systematizing Ba:

Corresponds to the Combination phase. &e authors refer to the Ba as “virtual space, where information technology facilitates the recombination of existing explicit knowledge to form new explicit knowledge” (Nonaka & Konno, 1998).

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&e Systematizing Ba is especially relevant because the idea market represents a digital virtual space where all participants can organize around ideas and create new ideas. Because the knowledge conversion is made up of codi"ed, explicit knowledge, it can be distributed throughout the whole organisation without loss of meaning. Systematizing Ba, thus takes places on the largest scale of all the processes. (Example: Mr. McAfee takes the new concept to his own domain, enterprise tools, and coins the term “Enterprise 2.0”. Mrs. Hamburg mixes the concept with her own domain, Human Resources, and coins the term “HR 2.0”.)

!e Exercising Ba:

Corresponds to the Internalization phase of the SECI model. Explicit knowledge is converted into tacit knowledge. &e cycle of knowledge creation returns to the individual as the explicit knowledge is internalized. &e new knowledge becomes ‘known’ by the ‘knower’, and thus becomes a synthesis of the created explicit knowledge and the ingrained tacit knowledge of the knower. Again, new knowledge is created and the cycle continues.

Fig. 1 below shows two dimensions of Nonaka’s model, the epistemological and ontological. &is indicates the type of knowledge and the level at which it is known. &e illustration shows the SECI and Ba models to give the reader an illustration of the correspondence between the two models.

Fig 4. The dimensions for understanding Nonakaʼs knowledge creation theory.

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Figure 5: SECI model side by side with the Ba model Nonaka & Konno 1998: Process and Context.

Finally, if the Ba model is inserted on the planes of the epistemological and ontological dimensions, the figure will look like this:

Figure 6: The Ba model in the two planes of knowledge, epistemology and ontology (Source: Nonaka & Konno 1998)

Thus a combination of the two show where and how the different kinds of knowledge are converted and created. What is interesting to note with regards to idea markets is the Cyber Ba, where groups and individual interact across the organisation, thus opening a potential for collective intelligence. However, in Nonaka’s framework, the knowledge conversion process in solely between explicit types of knowledge. One of the reasons for choosing the SECI framework is that it might be used to illustrate how idea markets make it possible to include tacit knowledge, even at this ontological level.

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CRITIQUE OF THE SECI FRAMEWORK

&e SECI model has been criticized from a number of sources through the years (Gourlay, 2004). At the base of the argument is the epistemological distinction between tacit and explicit knowledge being too simplistic due to the knowledge distinction being binary, and generally showing little philosophical insight regarding the epistemic principles that are incorporated into the model (Zhu, 2006). Engeström (1999) points to necessity of having prede"ned problems and tasks to work with in order to move through the di%erent phases of the SECI model. &us the knowledge creation cycle is highly contingent upon these problems that then become crucial for innovation but are not included in the model. Gourlay (2004) points to empirical shortcomings when devising the steps in knowledge conversion between tacit and explicit knowledge. &ere is also critique of the empirical reality of four SECI phases. Gourlay states that the basis for formulating the four parts of the SECI model was based on a limited convenience sample without the use of any further qualitative studies. Culturally, the model has been criticized for being so deeply rooted in Japanese culture (Glisby & Holden, 2002), especially with regards to tacit knowledge, that it cannot be transferred to knowledge management and creation in other cultures. Finally, Breiter (2002, cited in Gourlay, 2004) points to Nonaka’s inability to convey how knowledge is actually managed.

While I do agree that Nonaka and his co-writers can seem to be picking and choosing the parts of philosophical principles that aptly "t or expand the SECI model, the writer/philosopher Nonaka should also be commended for the simplicity of the model. &e simplicity is why the SECI model has been chosen in this thesis. &e ability to break the knowledge creation process down into four conversions provides a simple frame of reference when presenting the impact of a tool such as idea markets. Furthermore, there is a clear ontological and epistemological distinction between the role of the individual and that of the collective. In relation to the theme of collective intelligence, this provides a clear background for highlighting the impact and change that tools for collective intelligence could bring to organizations. Finally, Breiter’s critique may justi"ed to a degree, but he does miss one crucial point. Nonaka is adamant that KM is about management for knowledge, not management of knowledge, a sentiment which is echoed in the ‘2.0’ ethos: Less management, more leadership.

INNOVATION

TYPES OF INNOVATION

An international survey conducted by BCG in 2009 showed that companies perceived to be innovation process leaders, such as Toyota (known for their process approach), Apple (design approach) and Google (human resources- internal market approach) on average generated 430 basis points more than their peers with lesser investment in, and focus on, innovation. &e concept has been very much a buzzword in the last decade or so, and many de"nitions of varying quality have arisen. &erefore, the "eld of innovation is one that can be characterized as ambiguous. &e word is o(en used interchangeably with ‘creativity’, ‘clever’, ‘new’ and other words of a similar vein. Dismukes (2005) states that “Due to the complexity of the phenomenon, no universally accepted typology exists”, however commonalities can be deduced from many of the scholars on the subject. However, a clear

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de"nition is needed in the thesis as idea markets and their impact on innovation is the focal point of the thesis. &is chapter aims to give a clear de"nition of the the concept of innovation and well as the principles of innovation. Finally the concept of inno#ation management will be inspected, as Enterprise 2.0 tools such as idea markets are expected to make a di%erence in this department.

According to the the Merriam Webster dictionary, the de"nition of innovation is;

1 :%the introduction of something new.

Peter M. Senge o%ers a de"nition of what innovation is, for businesses, and what it is not:

”Engineers say that a new idea has been ”in#ented” when it is pro#en to work in the laboratory. !e idea becomes an ”inno#ation” only when it can be replicated reliably on a meaningful scale at practical costs.”!(Senge 1990)

&us, the ability to create something new is not innovation, but an invention. When you are able to capitalize on the invention, it can be classi"ed as innovation. &is is interesting when looking at a company such as Google, which by is perceived to be one the most ‘innovative’ companies in the world. In SP/Businessweek’s Innovation Index 2008, the company "nished second only to Apple. However, when you look at all the inventions that the company has built, such as Google Docs, the webbrowser Chrome, Google Mail, and aquisitions such as Youtube, Deja News, Android, Jaiku, Blogger and Google Maps - as much as 95% (Anthony, 2009) of the company’s revenue can be traced back to Google’s golden egg, web based advertising. &is to point out that innovation is o(en perceived to be “ability to bring new things to market”.

In “Managing Innovation” Tidd et al. (2005) present the “4 P’s” of innovation to designate where innovation takes place in companies:

• Product innovation: Changes in products and services.

• Process innovation: Changes in the process of creation and delivery.

• Position innovation: Changes in context of product/service introduction.

• Paradigm innovation: Changes in mental models, framing organizational activity.

A further dimension of consideration for innovation is the “degree of novelty” (ibid) to the company and its environment. Innovations that gradually build upon their existing object in ways that can be outlined as a linear path are what is referred to as incremental innovations. Innovation inferring fundamental changes is called radical innovation. &ere seems to be an intuitive logic that giant leaps are better than small steps. However, innovation is signi"ed by the ability to capitalize on inventions, where companies exploit their abilities in the short term, to

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sustain the business by making continual, evolutionary improvements. Especially in environments of continual acceleration (Rosa 2003, Davenport 2005) the incremental steps are important as they help sustain the business while radical opportunities can be explored and eventuality capitalized upon in the longer term. In innovation strategy literature, companies that are able to strike an e%ective balance between innovations that respectively sustain and develop the company are referred to as ambidextrous organizations (O’Reilly & Tushmann, 2004). &e table below outlines the di%erences between radical and incremental innovation. As it can be seen, the knowledge creating processes that underpin the two types are very di%erent, O’Reilly & Tushmann describe ambidextrous organizations as those who are able to accommodate both types of processes. &is is done by creating structurally independent units with their own “processes structures and cultures” (ibid) yet remaining integrated in management hierarchy.

Incremental innovation Radical innovation

Linear progress

Improvement

Evolutionary

Sustaining

Exploiting

Uncertainty: Low

Formal process

Discontinuous progress

Creative destruction

Revolutionary

Developing

Exploring

Uncertainty: High

Informal, “Fuzzy” process

Table 3: Incremental and Radical Innovation , adapted from Bessant (2005)

In “Technological discontinuities and organizational environments” (1986), Tushman and Anderson juxtapose innovation and ability. &e notion of innovation having a competence-enhancing or–destroying e%ect is introduced here. &e dominance of technologies that are being incrementally bettered will mean that the resources and knowledge residing in a "rm will be of direct use, and possibly built upon. &is is referred to as competence-enhancing, while radical, discontinuous innovations that require di%erent knowledge and/or resources is seen as competence-destroying.

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Recently management literature has introduced di%erent types of innovation, notably Value Innovation, o(en referred to as Blue Ocean Strategy, (Maubourgne & Chan) and other strategies recommending to seek out uncontested market space and challenge market structures and assumptions.

&us the scope of innovation ranges from the organisation itself, the products and services it delivers, strategic maneuvering, restructuring and rejuvenating organizational knowledge and possibly any other action wherein parts of pro"t seeking organizations integrate change. As a means of delimitation, this thesis revolves around a focal point that is; product innovation and the associated procedures and strategic choices. &is is re#ected the methodical choice of linking case-studies (NPD procedures) with a systemic view of the organisation and its environment (Strategic choices).

Below is a matrix produced to illustrate the types of innovation and their impact. According the literature on innovation, a new business model (Voelpel, Kim & Marbourgne) coupled with a new technology (Tushman & Anderson, Dismukes) represents the opportunity for a radical innovation with the potential of upsetting the market – a disruptive innovation. When applied to actual innovations and their real-world market impact, it is clear that such a matrix is conceptual in its nature. Take the example of how Apple had an impact coupling the semi-radical ipod (it certainly was not the "rst mp3 player on the market) with a digital music reseller, Itunes Music Store. &is was not the "rst of its kind either, and digital music had been making its rounds on the web legally and illegally for years before. However the coupling of the two had an impact on the digital corner of entertainment industry.

Figure 7: A matrix to summarize the most common literature on innovation and impact.

Employee Driven Inno"ation

In the idea markets examined in this thesis, the platform serves as a setting for an internal idea sharing and evaluating initiative, involving only employees. In the conclusion, some of the other potential applications of idea markets will be discussed, such as letting customers participate in idea competitions and aligning companies

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strategically to share and discuss ideas between each other. Sources of knowledge on employee driven innovation includes a study by &e Danish Confederation of Trade Unions (LO) and a research paper “Employee Driven Innovation: &e Discovery of the Hidden Treasure” (Kesting & Ulhøi, 2008 - working paper).

When studying papers on Employee Driven Innovation (EDI) it is clear that prompting the employee for ideas for process innovation, o(en of the incremental type, is widely used. In “Employee-driven Innovation: Improving Economic performance and job satisfaction” (LO, 2008) three types of employee involvement are presented;

• Research driven innovation, where employees contribute with their knowledge and hand-on experience. In terms of the SECI-model (Which will be discussed in the following chapter on knowledge), this includes conversion from the tacitly held knowledge that may close the important gaps between theory and the real world, when coming to market with new innovations.

• User driven innovation, where employees contribute the information and knowledge gathered during interactions with customers of the organization.

• Finally, there is price-driven inno#ation, where the employee uses his or her experience with everyday processes to spot ine$ciencies to which suggestions for optimization can be forwarded.

MANAGEMENT OF INNOVATION

Idea markets are a tool for innovation management, therefore it is important to clearly de"ne the term. Innovation management is the tool for controlling or enabling the processes that lie in and around the di%erent innovation types. According to the Product Development Management Association (PDMA), upwards of 60% of the top performing companies in a 2006 study (Levine 2007) employ a Stage-Gate model (Cooper, 1990). Here management of the innovation process is divided into 7 stages; idea generation, assessment, de"nition, development, testing and "nally launch.

One of the de"ning features of the stage gate model is control. As ideas move forwards through the seven gates, they are subject to ‘stop/go’ meetings. &us, innovation management is made up of decisions, based on data, predictions and some cases perhaps hunches. &e stage gate model has been criticized for the rigidity of the system and managers’ tendencies to base the decisions at the stop/go meetings on statistics and other types of ‘safe’ archival data (Mootee, 2006). &e structure of Stage Gate idea management is in itself incremental, moving forward in 7 clearly de"ned steps and is thus only "tting for highly predictable processes with incremental outcomes. (Hall, 2007). Hall goes as far as calling the Stage-Gate model “Corporate Communism”. Illustrated in the drawing below.

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Figure 8 : Achtung! The decision meetings in the Stage-Gate model are often interpreted as highly rationalistic and positivistic with an emphasis on the predictable.

In the Stage-Gate model, and therefore a majority of innovation processes, there is a primacy on the decisions. Barnes (2007) points to these meetings as the strength and also the weakness of the Stage-Gate system. Firstly, there is the threat that corporate politics can act to heavily de-rationalize the proceedings at the meetings that constitute the decision-factor in the Stage Gate process. Conversely, a highly analytic, rational approach can slow down the process, impeding speed to market. (ibid).

Another critical point is the role of the decision maker or makers. However rational the grounds for any decision involving innovation will involve the complexity of interactions within company and possibly interactions with a market or other stakeholders. &us there is a high likelihood that actors taking such decisions will act on the basis of bounded rationality (Simon, 1961). With reference to the illustration above, a rigid stage gate approach is o(en taken because of the risk and uncertainty associated with innovation processes. &e emphasis on decisions entails that such decisions regarding uncertain processes should be as justi&able as possible. With reference to the illustration above, it is not to be expected that a prospected project will make through a gate because someone has a ‘hunch’ that the project is a killer idea. Rather, the decision will be based on formalized, predictable and explicit information. Data that is inherently backwards-looking. In regards tho the SECI model, the input for decisions in the stage gate model will be based on the the interactions taking place in the Combination phase of the knowledge creation model (Nonaka 1998), meaning there is a primacy on Explicit knowledge.

INNOVATION OF MANAGEMENT

In “&e Future of Management” Gary Hamel (2007) argues that most important type of innovation is management inno#ation, more so than operational innovation product/service innovation and strategic innovation. Such managerial changes can occur in knowledge management, internal communication and project management and number of other "elds (Hamel 2007). Hamel’s argument in the book goes that it is the "eld of management itself that is lagging behind in many 21st century companies. &e management paradigm still looks to e$ciency, cost-saving and incremental improvements such as prescribed by 20th century management writers from Taylor to Coarse and systems such as Six Sigma and Total 'uality Management. What Hamel prescribes is that the

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real long-term advantages in both war and business, lie the ability to constantly change way in which management is carried out. &ose who are able to instill passion, a distributed sense of responsibility for innovation, boldness and adaptability rather than rigidity, they win in the long term. Hamel cites an interesting case - that of Toyota and GM. In the 80‘s, American carmaker GM had sent their people to Japan in order "gure out what lay behind the Japanese competitor’s ability to produce cheaper and higher quality cars. What they came back with, was intricate details of Toyota’s Just in Time (later LEAN) processes. While it would now seem that they had the blue-print for copying Toyota’s e$ciency and quality, it never really happened. According to Hamel, this is because their paradigm meant that they missed one crucial point: People. Much in the same vein, Japanese management thinker and philosopher Nonaka (1998) has o%ered one framework for processes (SECI) and one for the context in which people actually "nd themselves (Ba). Enterprise 2.0 tools. such as idea markets "t this description. Not only do they change innovation management they also mean management innovation.

TYPES OF REASONING IN INNOVATION MANAGEMENT

In innovation management, especially in the more classic Stage Gate inspired models, two models of reasoning and justi"cation represent the norm: induction and deduction. Induction refers to the practice of reaching general conclusions based on facts and real-life cases. Deduction is the process of coming to conclusions, based on the premises of the internally sound, logic, arguments. Whereas induction bases itself on experienced facts, deductions can be inferred from reasoning, without actual experiences. Itoh (1996) argues that both models of reasoning are inherently backward-looking, just as they both base themselves on the con"nes of current facts and logic. Itoh thus broadens the spectrum of reasoning in innovations decision to abduction or abductive reasoning. With this model of reasoning, one sets objectives based on the assumption that ‘it must be‘ rather than ‘it is’. A(er this, di%erent hypotheses are tested to reach the ‘must-be’ goal. Itoh (1996) calls these goals Transcendental Hypotheses. As such, induction and deduction is o(en used as the basis in the process of abduction, however the process will be more creative and more iterative than the two former models that set out to ‘"nd the truth’.

ABDUCTION AND INTUITION IN INNOVATION MANAGEMENT

Idea markets may act as a catalyst for letting more tacit knowledge into innovation management, even though innovation management is a discipline that puts a primacy on control and justi"cation of decisions. Tacit knowledge is linked to intuition and abductive reasoning (Henry, 2001). Henry (ibid) argues that the ability to incorporate intuition in management is the key gaining a creative edge. Martin (2005) cites the successes of design oriented "rms as the ability to include abductive reasoning in the innovation process. Martin (ibid) writes: “Designers may not be able to pro#e that something "is" or "must be," but they nevertheless reason that it "may be." !is style of thinking is critical to the creative process.”.

It will be argued that idea markets can function to tap into the collectively held tacit knowledge of the organisation. Participants can help an idea garner more attention in the organisation, simply by investing in said idea. &ere is no need for explaining just why is seems like a great idea. &us there is a means for bringing ideas,

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values, suggestions or otherwise to the attention of the organisation with a lesser degree of formalization, thus rendering the organisation able to identify and act on ideas based on a ‘collective intuition’.

&e Transcendental Hypotheses can lead the innovation process to make jumps, such as it known in radical innovation, rather than follow the safe predictable incremental path that is most common in innovation processes. &e non linear, sometimes complex or chaotic process of abductive reasoning does not "t well with command and control systems, o(en linked to hierarch organizations and the Stage Gate model. A new innovation model, based on abduction with creative goals and reiterative attempts to reach those goals need to embrace or absorb (Boisot, 1999) the complexity in this process.

CREATIVITY IN COLLABORATIVE SYSTEMS FOR INNOVATION

More than o(en one will see creativity and innovation as interlinked notions. Amabile (1988) states that “innovation begins with creative ideas”. Amabile’s "ndings involve the e%ects of di%erent types of motivation, and the e%ects of play and competition on innovation. Taking up the "ndings of Amabile, Nonaka (1998) and their own, Robinson & Stern(1997) proposed six factors for corporate creativity. Stenmark (2006) has added a further two, giving eight elements that are used as a ‘check-list’ when evaluating idea markets as an innovation tool. &e eight factors are as follows:

Non-preconception principle

In line with the criticism of the Stage-Gate model. An common theme with innovation is the tradeo% between creativity and control. Losing preconception, means letting go of control and move from management towards leadership. Non-preconception is a factor that resides in the processes and culture in the company.

Skunk Works/Heterarchy

&e second factor in the framework for evaluating idea management tools is Skunk Works. &e term skunk works originates from Lockheed Martin, and dates back to 1943 (Lockheed Martin Website). In recent times, Google’s “20 percent time” is another example. Employees at the search-engine company are to spend one day a week working on projects that are not in their job descriptions. &is approach has lead to a number of Google’s successful products and new features in products in recent years. &is allows for a coordination of knowledge, guided by interest/passion, such Amabile prescribes (1983).

As such, the decision to allow employees to spend paid hours on skunk work rests with leadership. However, if such a decision is made, idea markets may help employees identify and organize around the ideas and project that are of interest to them and where they have applicable knowledge. Aside from skunk works, as put forward by Stenmark (2000), I would expand the scope of this factor with the principle of heterarchy.

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Heterarchy

“heterarchy n (Maths, linguistics) a formal structure, usually represented by a diagram of connected nodes,

without any single permanent uppermost node (from Greek heteros other, di%erent + arche sovereignty) “

(Source: English Collins Dictionary)

As a youth, perhaps playing too much football and studying too little, I was familiar with a concept that mimics heterarchy before I was even able to pronounce the word. In the 1974 World Cup, &e Netherlands had almost perfected an approach to football that was known as Total Football (Dutch: Totaalvoetball).

While the reality facing a company is far more complex than that of a football team, the organizing principle that the Dutch team used serves as a narrative as to how companies can leverage the idea of heterarchy, for innovation. &e idea of heterarchy is guiding for skunk works, and is a guiding principle for asserting if idea markets are a "tting tool for idea management and innovation.

Serendipity

&e third factor on the check-list for innovation tools is serendipity, which is o(en described as a happy accident or coincidence. Any system or process that raises the probability of such event occurring is supportive for innovation and creativity. Stenmark (2000) stresses that chance connection arising is one thing. It is as equally important to have the knowledge and wisdom to recognize the opportunity when it arises.

Diverse Stimuli

In continuation of the ‘serendipity’ factor, is ‘diverse stimuli’. Whereas serendipity refers to the connections made

“In Total Football, a player who moves out of his position is replaced by another from his team,

thus retaining the team's intended organizational structure. In this #uid system, no player is "xed in his nominal role; anyone can be successively an attacker, a mid"elder and a defender.”

“&e system developed organically and collaboratively: it was not down to coach Rinus Michels, his successor Stefan Kovacs or [captain and star player]Cruy% alone.”

(Source: Wikipedia)

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in the company, this factor is focused at the inspiration and information that the employee is exposed to. In innovation literature, the e%ects of ‘diverse stimuli’ is mentioned in Frans Johansson’s 2004 book ‘&e Medici-E%ect’. Johansson cites historic cases of how being placed ‘at the intersection of ideas, concepts and cultures’ ( Johansson, 2004) has lead to break-throughs and widely adopted innovations. Whether it’s Darwin’s &eory of Evolution, Personal Digital Assistants (PDA) and even Mike Old"eld’s ‘Tubular Bells’, great innovations have come from cross-pollination of ideas form di%erent subjects. A recent example is how the designers of NASA’s designer have been collaborating with experts in the "eld of Japanese paper-folding art form, origami, to create satellites that are even more compact. (Nasa Science Blog, 2007 ). In relation to idea markets, it can be assumed the a large company, where employees have di%erent backgrounds can lead to input of diverse stimuli for the participants.

Internal Communication

&e "(h factor again relates to the structure and frequency of internal communication within an organization. Stenmark argues that the creative potential of an organisation is correlated to the number of people. However, as the size of a company grows a fall in regards to internal communication is o(en observed (Robinson & Stern, 1997). Robinson and Stern put it:

“a company’s creative potential increases rapidly with its size, but that without systems in place to promote unanticipated exchanges of information, this potential will never be realized.” (Ibid).

It is expected that idea markets can act as a platform where unexpected information exchange takes place, and thus comply with the "(h factor of a system that enhances creativity and innovation.

Reciprocity

&e sixth factor is one that Stenmark has added to Robinson & Sterns initial factors for corporate creativity and innovation. Reciprocity between the participants in the idea market in an idea market that is implemented in a company may have distinct upsides when compared to other constellations or groups in digital networks. &e main reason is that the people are tied together not just by their participation in the given network, the idea market. &ey also work at the same company and thus have a common interest in the company succeeding in the future.

Motivation

With regards to the seventh factor, motivation, one can discern between intrinsic and extrinsic motivation.

Extrinsic motivation in idea markets are probably the most tangible phenomenon. Exogenous factors such

as the ability to win a competition and a prize is thought to be motivating factors to make participants join

in. An extrinsic factor that could be even more important is recognition. Recognition from all the peers

participating in the idea markets as they react to ones ideas is one the features that differentiate idea

markets from other idea management systems. Philosopher Axel Honneth (2004) cites recognition as the

most important engine driving society forward, and the same effect may take place, albeit on a smaller scale

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in a company using idea markets.

However, research into idea markets (van Bergen & Wennström, 2008) has shown that intrinsic factors

‘enjoyment’ and ‘challenge’ are predominant responses when participants are surveyed on their perception

of the idea market. In the three cases that will be used in this thesis, it will observed if the intrinsic

motivational factors play the same role as van Bergen and Wennström’s findings. In regards to innovation,

Amabile’s (Amabile, 1983) finding, the ‘intrinsic motivation proposition’ is of interest here. In the

mentioned work, Amabile came to the now widely accepted conclusion that intrinsic motivation is the type

of motivation when it come to creativity.

Rich Information Pro"ision

The final factor for creativity in the check-list based on Robinson & Stern (1997) and Stenmark (2000) is

rich information provision. What is meant here is the access to, and presentation of, information and the

ideas. Today, the web and enterprise 2.0 applications such as idea markets stand a great chance to make

such provision possible.

FACTOR RELEVANCE FOR IDEA MARKETS

Non-preconception principle Rigid, linear processes are commonly referred to as one of the most detrimental factors for creativity and innovation. Such processes are a result of preconception. In idea markets there may be categories or themes, if these are too sharply formulated, it may an example of preconception. However, this relies on the context of the idea market, not he market itself.

Skunk works Emergence tightly linked to the idea of ‘skunk works’. In skunk works, the hierarchy is suspended and employees are allowed to organize around ideas and projects, heterarchially.

Serendipity One of the underpinnings of innovation in the organisational setting is the tradeo! or the balance between ‘creativity’ and ‘control’. While we cannot control, or foresee many creative acts, we can give rise to the probability that a ‘happy accident’ occurs.

Diverse stimuli By letting members form many areas in the company partake in idea markets, we seek for each idea and each participant to receive feedback from many points of view.

Within company communication Information should be let out of the ‘silos’ of many corporations, because ideas can be greatly enriched by cross-pollination from di!erent points of view.

Reciprocity By reciprocity, Stenmark means the person to person relations in the intranet. In idea markets, everyone has a pro"le and the same possible impact on the market. #us we assume there is a high level of reciprocity. #ere may be factors inhibiting reciprocity, such as ‘real life’ status outside the market. #is may hinder reciprocity.

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FACTOR RELEVANCE FOR IDEA MARKETS

Intrinsic motivation While the extrinsic motivational factors are clearly evident in the

Rich information provision #e latest and most popular ideas are presented to the users upon logging in the web based idea market. #e ability to post link from the World Wide Web means that there is a multitude of information presented and available in idea markets

Table 4: The 8 factor for creativity in intranets.

COLLECTIVE INTELLIGENCE

When studying secondary literature on prediction markets, idea markets and enterprise 2.0 as well as look at the marketing e%orts of idea market providers (www.inkling.com) it is clear that the term of ‘collective intelligence’ is linked to the phenomenon studied herein. &is prompts a closer inspection of collective intelligence.

I use Francis Heylighen’s de"nition:

“Collective intelligence is the result of the proper aggregation of local information in generating a global solution to a problem that is more optimal than what any individual could have pro#ided” (Heylighen, 1999).

In the de"nition of collective intelligence given, there are two factors: the crowd and the aggregation mechanism. (Watkins 2007). &e digital evolution has provided the basis for a wide array of mechanisms that aggregate the knowledge of crowds to form collective intelligence in Heylighen’s de"nition. Examples of collective intelligence and their associated aggregation mechanisms are given by Watkins (ibid), however the table has been expanded to include wiki’s and idea markets.

Collective Intelligence Systems Aggregation Mechanism Example

Recommender systems collaborative filtering (often implicit)

Netflix (movies) Google (webpages) Last.fm (music)

Voting systems direct democracy Elections

Folksonomies collaborative tagging Delicious (bookmarking)

Prediction markets market scoring rule Newsfutures (news) CFO markets (industry news) Iowa Electronic Markets (Political)

Wikis collaborative editing and peer review

Wikipedia (dictionary)

Idea markets market scoring rule / collaborative filtering / direct democracy

Spigit, Idea Excange, Qualcomm Innovation Fest,

Table 5: Aggregation mechanisms. Adapted from Watkins (2007) (Wikiʼs and Idea Markets added)

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As the reader may take note of, I have proposed that idea markets encompass three types of aggregation mechanisms. &is is a proposition linked to the di%erent types of behavior in idea markets. &is is of of the interesting points to idea markets. More in this to follow in the chapter on analysis.

All of the above systems represent what I have chosen to call instrumental collective intelligence. When companies make use instrumental collective intelligence it is o(en referred to as Crowdsourcing. &e term crowdsourcing was coined by Je% Howe, an in#uential writer on technology. Howe writes :

“Crowdsourcing is the act of taking a job traditionally performed by a designated agent (usually an employee) and outsourcing it to an unde&ned, generally large group of people in the form of an open call. [...] !e Soundbyte Version: !e application of Open Source principles to &elds outside of so'ware.” (Howe, 2006).

However, writers on the topic o(en add another dimension to collective intelligence which deals with the phenomenology of participating in collective intelligence. Echoing the split between the SECI model and Ba concept. &is side of collective intelligence may be less explicit than the instrumental understanding of the phenomenon. However, because of the importance of the social aspects of innovation (Hellstrom, 2005) and the perceived importance of tacit knowledge in knowledge creation (Nonaka, 1998) this dimension will also be covered.In “Collective Intelligence: Mankind’s Emerging World in Cyberspace”, Levy (1997) elaborates on the concept of collective intelligence :

“This new human dimension of communication should obviously enable us to share our knowledge and acknowledge it to

others, which is the fundamental condition for collective intelligence. Beyond this are two major possibilities, which could

radically transform the fundamental data of social life. First, we will have at our disposal simple and practical means for

knowing what we are doing as a group. Second, we will be able to manipulate, much more easily than we are able to write, the

instruments for collective utterance.”

Whereas the explicit version of collective intelligence is instrumental and refers to the use, the experience of participating in, and having access to, collective intelligence is di%erent. I call this experiential collective intelligence. &is is related to the concept of collective consciousness. &erefore, I distinguish between two manifestations of collective intelligence:

Level of Collective Intelligence

Function Primary Knowledge Type

Knowledge Creation

Instrumental Crowdsourcing Explicit Shared Process (SECI)

Experiential Collective consciousness

Tacit Shared Context (Ba)

Table 6: Collective Intelligence on two planes, how it functions and how it is experienced

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TYPES OF COLLECTIVE INTELLIGENCE

Before going on to describe and assess how collective intelligence (CI) may be integrated in NPD, it is important to note that CI can represent di%erent types of group interactions, which will be of concern when designing prediction markets for use in the enterprise. 5 di%erent type that have been proposed by George Pór (2004) are:

Dialogic Collective Intelligence “A diverse group of participants suspend their old mental models and engage in dialogue that values the emergent whole higher than its parts. Variations of this approach include Bohmian dialogue, “generative conversation” and “enlightened communications”(Pór, 2004). In this sense, CI is themed at a behaviorist, social interaction level, which points to one of the most important yet o(en inde"nable qualities that innovation managers are urged to pursue, a culture that enables innovation and increases the likelihood of innovation being successful – in short openness to change. Being alert of the groups method of collectively processing thought and seeking a state of disregard for normative structures be they socially or industrially constructed points to complexity being actively disregarded as the organization observes and makes sense of itself and its environment. A communicative situation aligned with Bohm dialogue essentially equates to learning and in this case collective learning. In “&e Fi(h Discipline” Peter M. Senge points to the importance of conversation, dialogue or communication and turn to Bohm in saying that Bohm “is developing a theory and method of "dialogue," when a group "becomes open to the #ow of a larger intelligence." special conversations that begin to have a "life of their own," taking us in directions we could never have imagined nor planned in advance. But these experiences come rarely, a product of circumstances rather than systematic e%ort and disciplined practice.” (Peter M. Senge, &e Fi(h Discipline, 1990).

Relevational (emergence-based) Collective Intelligence - Another type of collective intelligence, that may be important to bring forward, is relevational collective intelligence. 'uantum physicist David Bohm, again, coined the term relevation to describe the situation where situations can arise from what Bohm calls ‘implicate order’ (Bohm, 1981). George Pór tells us;

“As a form of collective intelligence this may be most vividly displayed by one person saying something and another person mis-hearing it in a way that pro#ides them with some answer or insight. !e answer, which was never spoken, relevated out of the space between them, drawn into existence by the second person's desire to know that answer. (Por, 2004)”

Understanding this type of collective intelligence through the perspective of collective intelligence through the lens of Nonaka’s SECI model, one might imagine a person coming with a suggestion that can be understood from a number positions, according to the experience, information and knowledge that a given perceiver of said idea would have. &us, when an idea is brought into the open, made explicit, the tacit knowledge that it is subjected to by means of the collected knowledge and judgement in the group, and members will judge and develop that idea in ways that resemble the emergent pattern that is held to be a central tenet to Enterprise 2.0.

Returning to the language of innovation, Stenmark (2007) tells us that serendipity, or the happy accident, is not something we can control. What can be done is to raise the likelihood that such event will occur. An argument that

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goes in favor of idea markets, is that they can act as spaces that allow for relevational collective intelligence to play out.

Pór touches upon four other types of collective intelligence:

Co-e!olutionary CI – “&is form of CI builds on the power of such evolutionary mechanisms generating intelligence over time as trial and error, di%erentiation and integration, competition and collaboration, etc. Its examples include: ecosystems, sciences, and cultures”(Pór, 2004).

Flow-based CI – “A group of people become so absorbed in a shared activity that they experience being completely at one with it and one another. Ensembles, high-performance sport teams, astronauts, and others in that state of communion, report on both an enhanced state of autonomy, and collective intelligence.” (Pór, 2004)

Statistical CI – “Individuals thinking and acting separately in large crowds can reach successful conclusion about their collective cognitive, coordination or predictive challenges. Examples include the “intelligence” of markets and cases popularized in the “Wisdom of Crowds” by James Surowiecki.” (Pór, 2004)

Human-machine CI – “&is form of CI leverages the synergy of the human mind and its electronic extensions, drawing on the best capacities of both. &e “collective” includes symbiotic networks of humans and computers working together and developing compound capabilities. It can also support all other forms of CI. “ (George Pór, 2004)

&e begging question is whether this knowledge can be extrapolated from the notion of collective wisdom over political prediction markets, fair-type estimates and bestseller books and into innovation purposes by means of Idea Market. Speci"cally it is of interest if and how they can be leveraged towards the innovation e%orts that are notorious for being unpredictable and intangible, requiring decisions to be made on very few facts, causing a high element of risk.

CORRESPONDENCE BETWEEN BA AND COLLECTIVE INTELLIGENCE

As a part of the literature review, and before looking at the results and outcomes of idea markets, two key concepts are juxtaposed: Ba and collective intelligence. In “Collective Intelligence : &e Invisible Revolution” Jean Francois Noubel writes;

“Original collective intelligence transcends and includes the individual. It transcends as a di$erentiated emerging entity appears; it includes the individual in a harmonious relationship that fosters his/her e#olution and pro#ides him/

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her meaning.” (p. 19).

Fayard’s westernized understanding of the Ba concept:

“A ba could be memorized and opened to a continuity of relation within a kind of atmosphere that refers to a particular feeling linked to a community's shared space and time. [....] In this perspective, a ba could be assimilated to a sort of level of consciousness. Ba pro#ides a platform that a transcendental perspective integrates all transformation needed. Ba may also be thought as the recognition of the self in all.”

(Fayard 2006, p. 26, emphasis added)

In other words, the ability for the individual to transcend is key in both the collective intelligence and the understanding of what can be called ‘a successful instance of Ba’. While it may be di$cult to point to actual proof of transcendence setting in for the individuals participating in idea markets, the concept bears importance as it has been to link incorporate both Ba and collective intelligence.

WISE CROWDS

&e recent proliferation of prediction markets coincides with the success of James Surowecki’s bestseller “&e Wisdom of Crowds”(2004). Surowecki highlights the idea that aggregating information from groups can be a superior platform for decision-making compared to deliberation between a select few, as seen on boards and in expert groups – the dominant type of decision making methods such as the Stage-Gate model. Roughly put, the median of collected estimates from a group can be remarkably close to the eventual outcome. &is can be seen in the precision of political prediction market outcomes and Surowecki incorporates a number of anecdotes in the “Wisdom if Crowds”. O(en mentioned is Francis Galton’s Ox (Surowecki 2004, p. 7). At a livestock fair in Britain the guests where invited to wager on the weight of a live ox which was on exhibition. Galton supposed that the outcome would be far of the actual mark, seeing as only few members of the crowd where actual experts in the "eld. Galton was surprised to "nd that the mean of the group’s guesses (more the 800 people) came within one single pound short of the actual weight of 1,198 pounds. It is noteworthy that no single guess was on the mark, however the median was that close. It should be noted that the “crowd-wisdom” demonstrated by the collective guessing the weight of the ox, can be described as statistical collective intelligence.

Surowiecki presents four criteria that must be in place for crowd wisdom.

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Criteria Description

Diversity of opinion Each person should have private information even if it's just an eccentric interpretation of the known facts.

Independence People's opinions aren't determined by the opinions of those around them.

Decentralization People are able to specialize and draw on local knowledge.

Aggregation Some mechanism exists for turning private judgments into a collective decision.

Table 7: The Wisdom of Crowds, Surowieckiʼs four criteria for “Crowd Wisdom”

&e "nal criteria, Aggregation, is handled by the market mechanism. Diversity and Decentralization can be handled by selecting crowds with di%erent backgrounds. In the cases that are used here, the crowds consisted of employees at three di%erent companies. However, as might be expected, the employees have di%erent backgrounds and di%erent opinions and views. &e criteria of Independence is harder to accommodate with the market mechanism. &e prices of the idea shares rise and fall as other participants in an idea market buy and sell shares. &us, Surowiecki’s criteria of Independence marks an important question for idea markets. &e criteria is tied to Keynes’ Beauty Contest idea and behavior in markets, which will examined in the "rst case.

In this chapter, I have made the judgement, that idea markets can be described in terms of collective intelligence, and the collective intelligence resides in two planes: &e instrumental use of, and experience of participating in - collective intelligence. &is makes an apt "t with Nonaka’s process and context of knowledge creation. George Por’s di%erent types of collective intelligence have been brought into play. &is will be important in the study of the implemented idea markets. Furthermore, it cannot simply if idea markets are, or are not, collective intelligence. Instead, one must attempt to understand to what degree the implemented idea markets are e$cient in bringing about the di%erent types of collective intelligence. &e chosen epistemic standpoint for addressing the actions that are taking place in the idea markets, is the SECI model. When using just the SECI model, I will address a speci"c idea generated in the idea markets and the knowledge conversion processes that take place. Furthermore, as the idea markets I have access to, and are knowledgeable of, are framed as competitions, I assert that the context of the idea markets are of importance. Continuing in this vein, I have asserted that enterprise 2.0 tools have a speci"c quality in their conduciveness towards emergence of collective intelligence.

THE MARKET AS AN AGGREGATION TOOL FOR COLLECTIVE INTELLIGENCE

&e application of the market mechanism is what makes the idea market a radical departure from conventional online suggestion-boxes. &ere are a number of arguments for using the mechanism in the knowledge creation process. Austrian Economist Friedrich Hayek in "&e Use of Knowledge in Society" (1945) argued that the market mechanism is the most e$cient method of aggregating, collecting and dispersing locally held information. Hayek's article was a rebuttal to another economist, Oskar R. Lange, a proponent for planned economy. Hayek contended that a central committee, setting prices could never match the speed and e$ciency of the open market, because no single individual could hold and make sense of all the knowledge that is dispersed in the collective group at any

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given moment. His argument is, that given the right conditions in a market, the principle of spontaneous self-organization would set in - and do so at a pace and scope that cannot be matched by a centralized organization:

“In ordinary language we describe by the word "planning" the complex of interrelated decisions about the allocation of our available resources. All economic activity is in this sense planning; and in any society in which many people collaborate, this planning, whoever does it, will in some measure have to be based on knowledge which, in the &rst instance, is not given to the planner but to somebody else, which somehow will have to be con#eyed to the planner[....]We must look at the price system as such a mechanism for communicating information if we want to understand its real function. [...] I am con#inced that if it were the result of deliberate human design, and if the people guided by the price changes understood that their decisions have signi&cance far beyond their immediate aim, this mechanism would have been acclaimed as one of the greatest triumphs of the human mind.” (Hayek, 1945)

&e polarity between central planning and markets allowing for self organization is also worth considering when it comes to innovation management. Top down governed hierarchal organizations easily create silos, wherein knowledge is isolated and is unable to #ow around within the organization (Gulati, 2007). A problem that consultants and scholars of knowledge management have been increasingly aware of as knowledge has become crucial factor to the development of a competitive advantage (Davenport, 2005).

&e principle of idea markets is to operationalize Hayek's insights in relation to knowledge intensive organizational e%orts such as creating, developing and selection of ideas for innovation, be it for product, processes or strategic moves (Beasant, Tidd, 2005). &e argument is that the market mechanism can be applied to knowledge management and creation at almost any scale and provide the bene"ts of spontaneous self organization, as put forward by Hayek. Interestingly, Jimmy Wales, had cited Hayek's article as what convinced him of the feasibility of the free open encyclopedia project, Wikipedia, that we all know and use today, "ve decades a(er Hayek’s essay was written (Lessig, 2005).

If the market can serve as an aggregation mechanism for the knowledge, information and opinions of large groups of people, the ‘wisdom of crowds’ may be harnessed. In a company, this would be the group of employees, interconnected in a network whose collected actions are mirrored in the market, the share price of each individual idea. &us, it would be possible to circumvent the hierarchical distribution of information and power. Describing this paradigm shi( in &e Cluetrain Manifesto (1999), Locke, Searls and Weinberger proclaimed the popular mantra that "Markets are Conversations". &is also rings true for Idea Markets. &e markets are in e%ect, a social space where everyone can share, discuss and evaluate ideas simultaneously. Every decision made by every participant will to some degree impact all participants, tied together by the market. &at means that the product of such a market, the ideas and their prioritization as a function of the individual ideas' share price, is the product of collective assessment, knowledge and intelligence. &e scope of markets as social space has been furthered by modern economists, who argue that the transactions and interactions that take place in the market have a social dimension to them, Virgil Storr (2008) calls this the 'meaningful extraeconomic conversations that can occur in markets'. &is view holds that the market-space is not just a nexus for exchange of good and currency, it is also a

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market-place where social interactions occur.

Finally, the idea market stimulates its users to keep checking in on their idea-portfolios as well as debate their ideas. In other words, they involve and retain users in contributing, developing and assessing ideas to a degree that has not been seen so far. Seeing as enterprise 2.0 tools such as idea markets are basically networks, they follow Metcalfe's law (Shapiro & Valarian, 1999), meaning that value can be extracted with increasing returns to scale. In other words, the more people who use them, the value can be extracted from them for the individual and for the volume and quality of information and knowledge, that goes into developing and evaluating the scope and potential of an idea.

THE CASE AGAINST MARKETS

&e basic economic principle set in motion when rewarding participants for successfully predicting what the average member of a group will prefer is reminiscent of a Keynesian Beauty Contest. &e idea of beauty contests in markets take spring from a passage in Keynes’ 1936 “General theory of Employment Interest and Money”. In chapter 12, “&e State of Long-Term Expectation”, Keynes writes:

Keynes gives an example where a newspaper features a competition, a beauty contest, where pictures of one hundred women who are competing for the title as the most beautiful. Readers are asked to pick six favorites. &ose who pick the most popular face, will enter a ra*e for a prize. &e question is, does this work as aggregation mechanism of the groups collective judgement? &is is dependent on the behavior. Do you perceive your entry as a vote indicating your personal ideals of beauty or do you see the entry as a chance to win the prize?

The actual, private object of the most skilled investment to-day is “to beat the gun”, as the Americans so well express it, to outwit the crowd, and to pass the bad, or depreciating, half-crown to the other fellow.

This battle of wits to anticipate the basis of conventional valuation a few months hence, rather than the prospective yield of an investment over a long term of years, does not even require gulls amongst the public to feed the maws of the professional; — it can be played by professionals amongst themselves. Nor is it necessary that anyone should keep his simple faith in the conventional basis of valuation having any genuine long-term validity. For it is, so to speak, a game of Snap, of Old Maid, of Musical Chairs — a pastime in which he is victor who says Snap neither too soon nor too late, who passes the Old Maid to his neighbour before the game is over, who secures a chair for himself when the music stops.

(Keynes 1936)

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Keynes hypothesizes that a participant will be rewarded for being less focused on what he or she consider to be the prettiest of the women. Rather, they should attempt to predict who the others in the market will choose. &us the factor that drive participation and therefore quantity of participants is also the factor that may compromise ‘honesty’ in the market. A modern day example of a Keynesian Beauty Contest may the music hit lists that are heard on the radio. Here, people are asked to vote for their top 5 songs from a selected list of new music. If users successfully have 3 of the top 5 songs in the overall list, they can win a prize, much like Keynes example. &e question is, what does the combination of voting and competing mean for the list as a representation of the Danes’ collective assessment of contemporary music?

As Keynes writes, this train of thought could go on almost go on as an in"nite regress depending on the size of the idea market. &e problem is that this could e%ectively nullify the positive e%ects of having dispersed, local knowledge as seen in F. Hayek’s “&e Use of Knowledge in Society. In other words, rather than having a virtual space where member of a given group will interact and organize around an idea based on their speci"c knowledge and judgement related to the idea or suggestion, participants will base their actions on presumptions about the rest of the group. &e behavior in markets is integral to our understanding of idea markets because this is exactly where they di%erentiate themselves from other groupware that lets members of organizations work on ideas collectively. Furthermore it is presumed that some of the evaluation that goes into investing in ideas will be tacit, based on hunches and will never be explicated and thus it gives us the opportunity to include the tacit knowledge of the members of said group when evaluating ideas. &us, it is proposed that Beauty Contest behavior can be detrimental to the perceived quality of idea markets.

PSYCHOLOGY AND RATIONALITY IN MARKETS

Endogenous Markets

Aside from the Beauty Contest, there may be other types of behavior and rationality in an idea market that are founded in psychology. &e idea markets researched in this thesis are fundamentally di%erent from prediction markets in that they completely endogenous. &at is, there are no outside factors that directly a%ect the prices of the idea stocks traded. As such, the idea markets studied follow the logic presented by Arthur et al. (1996) in the paper "Asset Pricing Under Endogenous Expectations in an Arti"cial Stock Market". Arthur and his co-authors present a theory of asset pricing that is based on the aggregated expectations of the agents in the market. Arthur et al. sees the psychology as “an ecology of beliefs that co-e#ol#e over time.”. In other words such a market will represent a Keynesian Beauty Contest, that does not end a(er one or two rounds, but rather one that is constantly

“It is not a case of choosing those [faces] which, to the best of one’s judgment, are really the prettiest, nor even those which average opinion genuinely thinks the prettiest. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be. And there are some, I believe, who practice the fourth, "(h and higher degrees.”

(Keynes 1936)

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running and wherein choices of all other participants are continually communicated to you. Given such conditions, and given a crowd of agents that are all seeing to maximize their pro"ts more than anything else, the market “self-organizes into a complex regime in which rich psychological behavior emerges.” (ibid). In other words, a market completely devoid of exogenous e%ects can set the scene for what Shiller and Akerlof (2009) calls social epidemics and contagion of ideas in feedback loops.

Feedback mechanisms

It presumed that the endogenous conditions seen in idea markets are thought to be highly susceptible to echoes of positive and negative feedback mechanisms or ampli"cation mechanisms (Tvede 1989).

&is raises the question of wether idea markets are e$cient. &e answer to this question may lie in the rationality and behavior of the participants. As Arthur et al. write “Under a low enough rate of exploration of alternative forecasts, the market settles into a simple regime which corresponds to the rational-expectations equilibrium of the e(cient-market literature.” (1996).

&erefore it is important to gain an understanding of the idea market as being either e$cient or signi"ed by psychological behavior. &is has been re#ected in the questionnaires sent out to all the participants in the idea markets in this thesis. Seemingly, the answer is: both. Around half of the participants in the idea markets indicated that they did not follow a track of rational, pro"t maximizing behavior, rather they would act as idealists by investing in ideas without attention to the share price of their ideas and they would not speculate in the potential popularity of the ideas. Rather they perceived their allocated idea market play money as votes rather than investments. What this means for the result of the idea market will be investigated in the "rst case.

&us the theoretical framework for understanding idea markets in the context of innovation is laid out. Knowledge creation as process and context is used to highlight the communication around the ideas. Collective intelligence is divided into instrumental CI, applied to the process of knowledge creation in idea markets, and collective consciousness applied to the Ba, the context of knowledge creation. &e principles of innovation and innovation management are laid out, and it will be examined and proposed how idea markets may function in relation to the two subjects. Finally, the behavior in markets must be studied to see if the knowledge creation process is e$cient.

CHAPTER 4!- METHODOLOGY &e selection of methodology in the thesis is based on the following assumptions:

• &e subject of the thesis is to examine idea markets as a means for idea generation. • Idea generation is a social and collaborative practice. (Menand, 2001, see the chapter on ideas a social objects)• &e Enterprise 2.0 ethos prescribes an emphasis on collaboration and emergence. (Hellström, 2006)• &e markets in idea markets built on endogenous expectation and are prone psychological e%ects (Arthur,

1996), therefore behavior and rationality are key principles.• Due to the social nature of idea generation, the experiences of the participants are presumed to be an important

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factor for the success and e%ect of idea markets.

&e term “emergence” is one that Hodgson (2000) has described as “macro-level system properties arising from micro-level interactions of system element without being planned or foreseen.” ('uoted in Tilebein, 2006, p.2). &e concept of emergence also lends itself to the notion that ideas for innovation are social constructs, formed through the interactions between people. Based on the assumptions, the choice of research design and research paradigms will be presented below.

Research Preperation

In order to gain more information about idea markets, a number of steps were taken. Firstly internet search as well as activity in various internet forums regarding Enterprise 2.0 tools and prediction markets. Secondly, a Danish prediction market developer and specialist, Nosco was contacted. Nosco had a few years of experience with prediction markets and idea markets. &e internet activity represents the large end of the funnel, and proved helpful in establishing contacts with US developers and users with some experience of idea markets in practice. &e contact with Nosco proved decidedly fruitful for the research. With Nosco, I was given the opportunity to work on three individual pilot projects in Danish companies who were testing idea markets. &e work consisted of many aspects such as workshops with clients, day to day support for the participants in the idea markets and "nally the ability to conduct evaluation of the three projects; a chemical company, Evaluation would consist of interviews and surveys of the participants.

Sample

&e contact with Nosco lead to access to three clients conducting pilot projects with idea markets.!In all three cases the Idea Exchange tool is implemented in trial period of 2-4 weeks, while a Best Idea competition was run. One of the major limitations of this study lies herein. As a research sample, a longer duration of use would be preferable, !however the three instances of idea markets that I have had access to, thus all three instances are convenience samples. Another weakness of the samples is that none of the three groups of employees had signi"cant experiences with other idea generation tools. &us many of the e%ects seen in the application of idea markets could re#ect the simple fact that the occasion for idea sharing and development had now arisen in the respective companies. &e competition element meant that participants who shared the ideas that ended up in the top with regards to share price, would win prizes. &e users who were best, most pro"table at investing their play money would be rewarded with prizes, too. In one case, the person to leave “the most valuable” comment in the system would be rewarded too. A(er the competition had ended, all three companies would select a number of the top ranking ideas and other ‘wild cards’ and formulate business cases based on said ideas. &e reward schemes and commitment to the winning ideas a(erwards varies amongst the three companies, and will presented in the speci"c descriptions of the idea markets.

In all three cases of idea markets, Nosco was tasked with conducting an evaluation of the idea markets based on criteria for succes such as share of users that participated, level of activity, number of ideas generated and users satisfaction. As a part of the evalution I had the opportunity to compose surveys and interviews for the

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participants in the survey. Aside from highlighting the criteria for succes, I was allowed to formulate insert my own research questions.

Research Design

Research design can be carried out in many di%erent ways. &e research strategy guides the methodological steps to be taken and such a strategy is re#ected in the researchers preconceptions of the problem at hand. &ere three most most common frames of research designs are, according to Yin(1994) and Zikmund (2000) are; exploratory, descriptive and explanatory.

Exploratory research is carried out when the area of research is signi"ed by high uncertainty. Yin (1994) states that such research should include a clear research purpose and a set of criteria for judging if the research has been succesful. Descriptive research is carried out when the area of research is highly complex. (Zikmund, 2000) Here, the researcher is to divide the ares of research into smaller components that can be quanti"ed and deat with statistically. Explanatory research is deployed when researching areas that are already covered to some degree, and where outcomes are already known to some degree. &us, explanatory research o(en deals with the ‘why’ type questions and will o(en serve to expand upon already existing theory (Zikmund, 2000).

Given that idea markets, and Enterprise 2.0 tools are such a novel "eld, the research is signi"ed by being exploratory. However the initial desk reseach has also lead to the formulation of three proposals dealing with the market mechanism and the knowledge creation process respectively. &erefore the research design is also represents features of the descriptive research strategy, as the overall problem has been divided into the three parts, which are studied on their own terms. &e "rst part deals with participant behavior in the market to conclude if the Keynesian beauty contest is in place. &e second part deals with the process of knowledge creation. Here, the idea is the focal point of the study, corresponding to the principle of ideas as social objects. Finally, the context of knowledge creation is the theme, and the Ba principle leads to a focus on experience.

Research Paradigms: #ualitative and #uantitative research

In research there is a distinction between two techniques, the qualitative and quantitative approaches. &e choice of approach is dependent on the research purpose and subsequent research questions.

'uantitative research is the most positivist approach. Here the researcher aims to describe, measure and predict occurrences that take place in the surrounding environment. Results based on quantitative research is expressed in numbers and formulas. Data in quantitative research is taken from samples, experiments. &e key principle of quantitative research is measurement, and the approach used in a wide number of "elds - most predominately the natural sciences. &e results are most o(en expressed in statistics, formula and tables. &e prototype quantitative researcher will remain as neutral, objective and technical as possible in the style of writing. &e knowledge generated in quantitative research is mainly formulated in terms of deductive reasoning.

'ualitative research has a more subjective nature than quantitative research. &e approach will o(en seek to 50

understand rather than predict the phenomena in the surrounding world. &e patterns inspected are related to behavior the factors that govern that behavior. In a way, the researcher will use him or herself as an instrument in the research. Approaches within the area of qualitative research encompasses constructivism, hermeneutics and humanism ( Johnson, 2004). Methods of data collection include ethnography, participation, interviews and action research and the results will be most o(en expressed in words and possibly pictures. Contrary to the quantitative approach, the qualitative researcher can opt to write in a style that is a “rich, thick (empathic) description, written directly and somewhat informally” (Onwuegbuzie et. al 2004). Yin (1994) proposes that case-studies are o(en deployed in case studies where goal is to attain a deep understand of the phenomena that is observed through the research problem. &e knowledge generated qualitative research is mainly formulated in terms of inductive reasoning.

Mixed methods research

&ough the two mentioned approaches are epistemically opposites, a number of researchers have argued that the two may well co-exist (Creswell, 1995). &e choice of one type of research may not necessarily preclude the use of the other. While purists of the two schools of thought will argue that the paradigms are incommensurable, a pragmatic approach is considered by Onwuegbuzie (2004). Pragmatism, such as it was formulated by C.S. Pierce and furthered by William James and John Dewey (Misak, 2000) is mainly concerned with observable practical consequences of the researched phenomenon. Following the assumptions that ideas are social objects and that endogenous markets are highly susceptible to psychological behavior (Arthur et al, 1996 and Shiller, 1999) as well as the fact that idea markets deals with large groups (crowdsourcing) there is a need for qualitative measures to understand the experiences of users and quantitative measure to describe the crowds in the idea market. &us, a pluralistic use of methods can be applied when looks for observable practical consequences of the given phenomenon. Due to the assumed social nature of ideas and psychological behavior expected in the markets, the stance of the thesis is mainly humanistic with quantitive measures applied to give a picture of the large groups of people participating in the markets.

!e choice of research methods in idea markets

&e problem at hand in the thesis is to understand how idea markets impact innovation processes in companies. &e begging question is: how is this measured? &e instrumental choice would be to implement an idea market in a selected company, and measure the impact on the bottom-line in the following years and deduce what value the innovations created in the idea market. &is would argue for quantitative, positivist approach to the research. However this approach is hindered by a limit of access and ability to exactly measure the impact of the innovations.

On the other hand, Enterprise 2.0 systems such as idea markets signi"ed by a high degree of social interaction. At the same time, in innovation literature, the social dimension of idea generation is reiterated by many writers. Hellstrom (2005) posits that innovation is social action. As has been posited, ideas for innovations can be seen through the lens of social objects, which is where Enterprise 2.0 tools provide a radical change over older tools and processes in the way the users/employees interact with the ideas and suggestions. &e possibility of generating and evaluating idea based on distributed intelligence based on networked processes rather sequential processes may

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lead to profound changes knowledge creation acts in companies. Such a change will be sought to be highlighted by understanding the process through the SECI model. Nonaka’s SECI model, and epistemology, puts a high emphasis on the cognitive dimension of knowledge creation.

&us the approach should make it possible to highlight the cognitive aspects of participating in the idea markets. &e behavior in the market as well as the experience of participating are to be studied, making the qualitaitve approach most applicable. Proposition 1, that the market aspect will turn the idea market into a Keynesian Beauty Contest is related to the behavior. &e second aspect, dealing with collective intelligence, is highly related to both experience and behavior. Finally, the aim of the thesis is to ‘rate’ idea markets in terms of an idea management tool perspective. &is will be based on Stenmark (2008) and the 8 point check list. &e factors that Stenmark propose are of a structural nature will be reviewed from observing the interactions in the idea markets. However, there are elements of the research that allows for quantitative research methods being applied also. Firstly, the contact with Nosco has given me the possibility of surveying and evaluating 3 implemented idea markets. It is also given that all members of the companies testing idea markets will be asked to "ll out an online survey where they are asked to respond to statements regarding their behavior and experience of the system. &erein lies the possibility of a quantitative comparison of the three surveys to give an indication of the robustness of the behavioral and experiential patterns. Furthermore, Nosco’s Idea Exchange employs an administrative module that gives access to a host of quantitative data such as; number of ideas and comments generated, user activity levels, time of activity. When found "tting these stats from the idea market will be referred to. &e pragmatist view and mixed methods approach is chosen, with an emphasis on qualitative data.

Research Strategy

&e concept of idea generation and idea selection and interaction around ideas was asserted to be best understood in qualitative terms, thus the qualitative approach was given emphasis. &e next point to consider is the choice of research strategy. Yin (1994) lists "ve types of research strategies for qualitative, social research. As can be seen in the table below, the di%erent types of approaches have their individual advantages in di%erent types of situations.

Research Strategy Form of Research Question

Requires Control over Behavioral Events

Focus on Contemporary Events

Experiment How/Why Yes Yes

Survey Who/What/Where/How Many/How Much

No Yes

Archival/Analysis Who/What/Where/How Many/How Much

No Yes/No

History How/Why No No

Case Study How/Why No Yes

Action Research How No Yes

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Research Strategy Form of Research Question

Requires Control over Behavioral Events

Focus on Contemporary Events

Participant Observation How/Why No Yes

Table 8: Research Strategies (Adapted from Yin, 1994)

With regard to the study of idea markets it is given that Nosco will ask all invited participants in the three companies to respond to a survey. In the dual role as researcher and project worker, I have the opportunity to add my own questions about the behavior and experience of the idea market. &is type of research is expected to yield both qualitative and quantitative data that will help in answering the problem statement as well as give information to help answer the three propositions. Aside from the survey, another methodological choice is made - the case study. Yin (1994) points out that a problem statement that addresses a phenomenon that tied to the context in which it exists. While it is the aim of the study to deduce general knowledge about the application of idea markets, each instance of idea markets are unique due to the uniqueness of the corporate cultures of the three companies. &e pragmatic, mixed methods approach is chosen because of the social, qualitative factors involved in idea generation and selection. Furthermore, the access provided by Nosco means access to three companies using idea markets. &e case study approach makes it possible to treat each as a unique phenomenon, while doing cross examination. Nosco’s Idea Exchange also gives access to quantitative measures which can help shed light on the qualitative data and information gathered. For example, in the "rst case, the subject is behavior in markets. While a qualitative interview regarding a single person’s behavior can be analyzed to some degree, the analysis becomes even more fruitful when the data from the survey highlighting behavior is added. Because of the availability of the three companies, the multiple case study method is applied. &is means that multiple units and multiple cases are presented. To illustrate the process, Yin’s process diagram is presented below, with the speci"c details of the present study added.

Figure 9: Chosen route for research strategy, based on Yin (1994)

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&e three investigations took place sequentially in September, November and December of 2008. &e sequential process means that the researcher can adapt the data collection methods if needed.

Data collection : Primary Data

Case studies allow for a pluralistic approach when it comes to data collection methods. Yin (1994) forwards three methods for data collection in case studies. &e table from Yin is seen below.

Figure 10: Data sources from Yin (1994)

Online Survey: &e work with Nosco and the implementations of their Idea Exchange tool has provided the opportunity to conduct a number of qualitative interviews as well as sending out electronic surveys from the survey service company, Polldaddy.com. All the invited participants, 281 people, were prompted to respond. In the survey, respondents were faced with statements about their experience with the idea market. Working as Respondents could grade the individual statements based on a four step psychometric scale (Strongly Disagree, Disagree, Agree, Strongly Agree). &is "rst survey, at the Multinational Chemical Co. was based on a Likert 5 step scale. However, it was found that up to 39% of the answers fell in the “neither agree or disagree” category. &us making it di$cult to decipher the reactions to the statements presented. &erefore I opted to remove the middle category, leaving respondents with a so-called forced choice to the statements. Aside from the scale, user were prompted with smaller text input "elds at the end of each series of themed statements. Finally the respondents

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were prompted with a larger text-"eld at the end of the survey for any remarks they might have that they had outside the the series of statements.

Theme Question Answers

Motivation - Intrinsic or Extrinsic motivation? (Amabile, 1988)

What motivated you to participate? Prizes, Element of competition, Influence, Leaderships motivation, To share ideas.

System interaction - Rich information provision (Stenmark, 2006)

Was the system easy or complicated to use?

Sharing, Commenting, Understanding share price of ideas.

Result How do you rate the winning ideas? The top ideas hold value for the company, My peers agree with ranking of ideas.

Experience - The Ba of the idea market (Nonaka & Konno, 1998)

How was the experience of the idea market in general?

Serious, Fun, Discussed ideas outside the system, Discussed competition outside system.

Trading Behavior (buy) - Analysis of ideas (Shiller, 2001) and perception of competitors (Keynes, 1936)

What made you invest in the ideas? Dialogue with colleagues, Ideas I genuinely believed in, Ideas others would like, Ideas I wanted the company to work with.

Trading Behavior (sell) - Analysis of ideas (Shiller, 2001) and perception of competitors (Keynes, 1936)

What made you sell you shares? The share price was falling, I found better ideas, I wanted to make a profit, The share price was too high.

Table 9: Motivation for questions in the digital survey, sent out to all the participants in the idea markets

Table 9 shows the questions in the online survey. See the appendix for the full survey. &e survey is devised to highlight participant behavior when investing in ideas. &is, in relation to the proposition about Beauty Contests. Furthermore respondents were prompted with statements about the idea market tool, Idea Exchange, their motivation to participate and their general experiences were inquired about as well.

Interviews : !&e interviews were conducted as part of the evaluation of the Idea Exchange pilot projects. An interview guide was prepared for the two interviews used in the research. &e face to face interviews were carried out a(er the the surveys, thus providing an opportunity to focus on any "ndings based on the responses hereto. In the two "rst cases, I chose to formulate one interview revolving around the chosen theme of the case. In the "rst case, with focus on participant behavior I had the opportunity to interview the winning idea trader. &e second case, presented the opportunity to interview the person behind the winning idea.

Participant Observation: Nosco’s Idea Exchange tool is an online platform, where it is possible to follow the event in the idea market live. As project manager and researcher, it has been a daily task to survey the system for any

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irregularities or such issues. At the same time, I was tasked with day to day support for users as well as daily contact with the respective project manager at the three companies.

!e Researcher: My role as both a researcher for this study and a project manager for the idea market consultancy Nosco meant that I was somewhat immersed in the implementations of Idea Exchange. In ‘'ualitative Methods in Management Research’, Gummesson (2000) advocates a stance and a method of action research (AR) where the roles of the academic researcher and the practitioner, the consultant, meet. Both work with knowledge, and therefore they face some of the same tasks. &is role is also discussed in Deluca and Galivan : “AR has the dual goal of bene"ting the research client and generating relevant research knowledge for the research community, making the synergistic outcomes desirable for practitioners and theoretical development (Deluca and Gallivan, 2008)”

Seeing as there is interaction with managers and users in the implementations of idea markets that will serve as cases, studies carried out in relation to this thesis re#ect the “Action Research” method. &e researcher does enact both roles, working with a company that develops and implements idea markets, while the writers has had a constant eye on the present thesis with regard to methodology, access and theory. However, Action Research has not been explicitly followed. In de"ning action research, it is noted that the researcher/consultant should actively interact with key personnel and change conditions surrounding the consulting and the research. In many instances, the role of the researcher/consultant in the present thesis is one of observation, not interaction. &at being said, there has been variations in the conditions of the three implemented idea markets. &ese are implications of the learning that has taken place in between the implementations.

A branch of qualitative research, information systems (IS) is of special interest. While the idea market system is many ways novel, it also a combination of phenomena from where theories can be extracted and knowledge. Aside from the focus on innovation and our knowledge of markets, it is also noted that the idea market is essentially an information system, allowing groups to interact. Gummesson places himself (He adamantly puts it “I”, not “&e Researcher” ) between the domains of the academic researcher and the practitioner or consultant (p. 2 in the introduction of “'ualitative Methods in Management Research ”). As a student writing my Masters &esis at Copenhagen Business School, while working with Nosco, the Danish prediction market and idea market specialist for a stretched period of time, has very much placed me in the very same position as Gummesson sets himself in.

Secondary Literature

Dick Stenmark of the IT University of Gothenburg has done extensive research in the "eld of creativity and intranet. His research is !substantial relevance to us, because creativity is inferred to be an underpinning of ‘innovation’. Furthermore, the idea market platform is essentially an intranet, in the cases examined, because it interlinks the employees of the companies in case. In his framework, Stenmark (2002) identi"es 8 factors that that are critical to corporate creativity and juxtapose these to the use of intranets. Using our "ndings from the cases and the data gathered, this framework is used to give a "nal interpretation of idea markets before presenting a framework of the factors that are assumed to be important for the success of an implemented idea market and the interplay between these factors.

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!e !ree Cases

&e researcher also has the role of communicator. Nielsen (1998) states that it is the researchers role to make the study and results as readable, and engaging as possible. In order to make the research as readable and understandable as possible, the three cases will each deal with one of presented propositions.

Case 1, which took place in a multinational chemical company based in Denmark will deal with the feature that characterizes idea markets the most, the markets and behavior herein. If too many users are active speculating in what others will like, there is a chance that someone else could speculate what the "rst speculators guesses are, thus skewing the information given by the market towards what people are guessing others like rather than what the actual participants in the market see as potentially great ideas for innovations. All three companies &erefore the "rst case will focus on behavior from all the three companies as well as a more qualitative focus via an interview with the winning idea trader in case 1: Multinational Chemical Co.

&e second case, taking place at a Danish department of an international engineering company producing pumps purses the concept of social objects and the knowledge creation process. &e theory itself can help simplify the understanding of so-called network e%ects on the process of knowledge creation. Instead of following the network from le( to right, up and down, the winning idea from case 2, &e Multinational Pump Co. is put center stage as the social object around which the members of the network can organize. &e concept of social object is further discussed in the introduction of the analysis, which is the next chapter. &e person behind the winning idea is interviewed - and like case 1, the survey data from all three cases is presented. &e SECI process of knowledge creation is used to understand the process of the winning idea from conception to "nishing at the top of the idea market. At the time of writing the Multinational Pump Co. has not acted on the winning idea yet, as this would provide more interesting information about the knowledge creation process from start to "nish. Nonaka’s SECI model makes it possible to understand the change in idea generation when a ‘collective intelligence’ approach of crowd sourcing is applied as opposed to a more linear approach.

&e "nal and third case describes idea markets in a broader, more holistic, perspective. &is case take place a Danish department of an international Pharmaceutical company. Rather than the instrumental approach to crowd sourcing and collective intelligence, the concept of Ba highlights the collective consciousness side of the matter. &e premises of the third case is: If the idea markets is a Ba, a space for knowledge creation, what signi"es it? Corresponding to the second case, it is also a way of asking: What are the conditions in which the social object is created and interacted with? &e researching of Ba is the case which puts the most emphasis on qualitative "ndings. In the researchers role as both researcher and practitioner, this is also the case where use is made of the observations made during the implementations of the three idea markets.

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Case Proposition 1:Market Behavior

Proposition 2:Collective Intelligence

Proposition 3Innovation System

1: Chemical Company Survey and interview used to gain an understanding of trading behavior.

(Data: Statistics from Idea Exchange, survey responses about trading behavior and interview with idea trader winner)

The researcherʼs observations.

2: Pump Company Idea markets as collective intelligence. Use survey and interviews to follow development of one idea.

(Data: Data from winning idea in idea exchange, survey responses about the ideas and interview with idea competition winner)

The researcherʼs observations.

3: Pharmaceutical Company

How can the context (Ba) of an idea market be described?

Are idea markets suitable as innovation systems?

(Data: Survey responses about reception of idea exchange, anecdotes from users, action research)

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CHAPTER 5: ANALYSIS

THE THREE CASES

Case No. 1 2 3CompanyDurationInvited ParticipantsActive ParticipantsIdeas SharedCommitment

Trader PrizeIdea Prize

Comment Prize

Chem. Co Pump Co. Pharma Co.3 weeks 4 weeks 10 days

392 250 95

159 79 92

363 72 91

Top !ve ideas as business cases

Top 3 ideas as business cases

Top 3 ideas as business cases

Gi" worth 8000 DKK Gi" worth 1500 DKK Token prize for top 10Gi" worth 8000 DKK Gi" worth 2000 DKK Gi" worth 5.000

DKKnone none Gi" Worth 2.500

DKK

Table 10: Overview of the three cases of the thesis.

CASE 1: MULTINATIONAL CHEMICAL CO. - CASE OF A BEAUTY CONTEST?

&e multinational Chemical Company is situated in Denmark, however it has o$ces in 47 locations around the world. &e company’s main product development e%orts are centered in Denmark and with Middle Eastern and Asian Paci"c concentrations too. According the conducted survey, employees had little or no prior experience with idea sharing procedures or tools. Innovation-wise the focus in the company is mainly product oriented. &e market in which they compete deals with coatings and paint, and technological innovation in these "elds is typically associated with R&D e%orts, carried out by people in white lab coats.

&e Chemical Company used the idea markets platform framed as an “Idea of Year” competition, wherein 80 of the company’s employees were invited to come up with ideas for new processes, products and markets. &e participants were invited from 15 di%erent departments around the globe, all with an expertise in paint solutions, some in technical areas others in marketing areas.

&e idea market was open for 4 weeks. When the market closed, which happens at an undisclosed time, the Chemical Company had a shortlist of the ideas. &e list represented the collective assessment of the ideas and suggestions that held the most potential for the company and that the employees showed the most interest in. &e survey was sent to all 136 participants and 35 chose to answer.

Introduction:

As noted in the chapter on collective intelligence, there are a number aggregation methods for harnessing the

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collective intelligence, markets being one. Others included implicit selection, as when Google ranks the pages in your search according to the actions of others performing the same search, or voting systems giving direct democracy. &e theme for this case is an evaluation of the market mechanism. On the one hand, the market mechanism holds the promise of transmitting local knowledge. Smith called it an invisible Hand, Hayek called it spontaneous order - principles of self organization. If such a self organizing principle can be instilled in the knowledge creation process by using the market mechanism as the aggregation device for collective intelligence, the upside of using idea markets becomes very apparent.

&e other side of the of the argument is based on Keynes’ notion of a beauty-contest mentality rationale setting in. &e idea market is a self-resolving market as opposed to the event driven derivatives, where the value of stock will be based on actual event taking place outside the market. Because the market is self-resolving, traders in idea shares are solely rewarded for predicting how the other traders in the market will react. As mentioned in the theoretical chapter, this sets the scene for beauty-contest rationale to set in. &e e%ect of this can be an idea market where the share price of the ideas do not represent the aggregated local information, knowledge and judgements of the participants. Rather, the share prices will represent the guesses of the users as they attempt to predict what everyone else will see as a good idea.

To gain an understanding of participant behavior when buying and selling idea shares, the survey presented the respondents with the following statements and the share of positive responses (either + or ++) :

Statement Multinational Chemical Co.

Multinational Pump Co.

Multinational Pharmaceutical Co.

I invested based on conversations with my colleagues

8,3% (20% neu.) 39% 25%

I brought shares in ideas I genuinely believed in

69% (10% neu.) 67% 92%

I invested in shares I assumed my colleagues would invest in*

23% (12% neu.) 35% 24%

I invested in the ideas I wanted the company to act on

79% 65% 94%

I sold because I found a better idea to invest in 72% 44% **I sold because the share price was falling 38% 47% **

* In the !rst survey, the formulation of statement was somewhat more direct: “I disregarded the quality of ideas, I simply played to win”

** A technical error in the polling systems meant that these results showed up as an error.

It is noted that in the !rst survey, the respondents were asked to grade they compliance with the statements based on the 5 step Likert scale.

Table11: From the questionnaire: Trading behavior

Initially the response that is most interesting for locating Beauty Contest rationale. An average of 27% of the 60

respondents indicate that they invested based on assumptions about what others would invest in rather than their personal judgement of each idea. &e opposite response, indicating behavior where the participant would invest in ideas they genuinely believed in, represented a majority of the responses. An average of 76% of the responses in the three cases answered positively to this statement.

Interpreting Keynes’ words in terms of the idea market provides three classes of participants: &ose who buy shares in ideas that they genuinely believe in, those who speculate in what others will invest in, and "nally2 those who speculate what the speculators will assess to be worth investing in. From this, one can see the sketching of two distinct types of participant behavior. &e Speculator and &e idealist. &e concepts are derived from Keynes’ de"nitions of trading behavior in his 1936 “General theory of Employment Interest and Money”.

&e naive participant, as Keynes’ calls it, is the one who invests in ideas (or picks the prettiest girl) based his own taste and judgement. If everyone in the market behaved as what i chose to call the ‘idealist’ rather than the ‘naive participant’ it would be expected that the share prices re#ected the collective genuine assessment of the ideas in the idea market. &e other type of participant behavior is the one who devotes his or her intelligence to determining what shares the general crowd will prefer in the idea market. &e ability to successfully predict the general choices of the participants will be rewarded, by the logic of the market, in pro"ts on the shares invested in.

An interview with the winning idea trader in the idea market was carried out. &e dialogue was focused in the winners’ trading strategy and his general observations on participant behavior and reception of the idea market.

"e Speculator / Technical AnalysisIn the interview with the winning idea trader from case one, the respondent stated:

“What I did was in#est in ideas, so that they would make it up the ranking. I think some players behave as we do when searching for things using Google. We always pick a link "om the &rst page we see....What I did, was &nd ideas in the bottom of the ranking, new ideas that no-one had noticed yet - and I in#ested in them so that they would come to page one, just like Google. When enough people had in#ested in the idea so that the price had gone up - I sold my shares and found a new idea to in#est in "om the bottom....But of course it had to be ideas that I could #ouch for, and that I supposed others would see value in too”.

Pursuing this strategy has produced winners in the trader competition in two of the three cases seen here. While a participant in a idea market is not very likely to be motivated by winning the prizes alone (van Bergen and Wennström, 2008, p. 60), the behavior of participants who openly to win the trader competition does in all three cases imply that the structure of the competition and the market does to some degree mimic that of a Keynesian Beauty Contest. &e quote above also implies this, as the participant could not buy just any stock, but one that he supposed others would invest in, once it was brought to their attention. As seen below, 23% of the participants in

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2 In the following, the latter type of speculator has not been included, simply because the writer does not find it realistic that any participants have engaged in such line of

thought, nor has such speculation been evident in the surveys or the interviews.

the case agreed that their behavior in the idea market was founded in an analysis of what general preferences would be. &e speculator can be said to follow a rationality of technical analysis (Shiller, 2001) where the investor will base analysis of idea shares on market expectations rather than the idea itself. (Ibid). However, the speculator may also attempt to pro"t on the positive feedback loop (Tvede, 1989) that can come in place if an idea reaches the top of idea rankings. As was seen, the winning participant in the interview relied on this analysis.

"e Idealist / Fundamental AnalysisIn the survey, it is seen that a majority invest in ideas and suggestions that they “genuinely believe in” and a majority also indicate that they invest in ideas that they “want the company to go forward with”. &e sentiment was also seen in many of the qualitatively open inputs in the survey, where respondents in all three cases have let it be known that they see the idea market as a way of being heard. It can be assumed that the Idealist sees his or her play-money more as a means of casting votes rather than making more play-money and eventually taking one of the prizes set out to motivate participants to join the idea market. Furthermore, the idealist is expected to follow a rationality of fundamental analysis (Shiller, 2001), where the traders invests in an idea based of the perceived potential for the idea, rather than expected volume of trade based on the ranking of the idea or other knowledge.

When the Idealist/Speculator distinction between approaches to the idea market used, one can explore the relationship between the two. One can assume that idea markets in the setting provided in the three cases, the organizational setting, and the framing as a competition is likely to invoke roughly the same distribution of the two approaches. In case 3, the concept of Ba will be addressed, the social space of the idea market. &is will help us towards an understanding why a majority of participants behave in the naïve way, as Keynes would call it. But for now, the distribution is taken for granted and the dynamics between the two approaches are analyzed.

If it is assumed that a group of of participants that show &e Speculator’s traits will represent a share of 26-40% of the collected group, thus the Idealists are likely to sit on the majority of the assets in the market. In other words, a main source of pro"ts, is the ability to predict the actions of the Idealists, just as the winner in the excerpt from the interview indicated above. If the Speculator invests in shares in ideas or suggestions before the Idealist invests in it, he or she will gain a pro"t, as new participants invest in the idea and thereby push up the share price due to the principle of demand.

If one breaks down the dynamics in these interactions, the Speculator is dependent on the Idealist. However, as was heard in the excerpt, a common approach is to "nd a not yet rated idea, invest in it in order to make the share price rise, putting the idea on the "rst page, garnering more attention from participants.

I invested in shares I assumed my colleagues would invest in

I brought shares in ideas I genuinely believed in

0% 20% 40% 60% 80% 100%

69%

23%

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! ! ! ! ! ! ! ! Trading behavior in case 1

"e Idealist Speculator?When one discerns between Speculators and Idealists, is also important to note that a number of interviewees indicated that they were not exclusively one or the other. &e principle of the two types were observed on background of Keynes’ aforementioned text, but when prompted in the survey, the two choices “I invested in ideas based what I thought my colleagues would invest in” and “I invested in ideas that I genuinely believed in” were not mutually exclusive. From the data complied in the surveys, 12-20% of the Speculators also said they invested in ideas they genuinely believed in. In fact, in the interview with the winner, the following was shared:

“In the beginning, I de"nitely just bought to win, but as my fortune grew, I realized that I would have quite an impact when I invested in my last shares. &erefore, I actually put my money on the two ideas I liked the most.”

&e reason for interest in observing Beauty Contest behavior is Surowiecki’s assertion that the the conditions for ‘Crowd Wisdom’ are that there is a diversity of opinion, independence between agents, decentralization and some form of aggregation. &e issue at hand is, that if the Beauty Contest behavior becomes predominant, the market will not be e$cient. What would this mean for the crowd-wisdom aggregation mechanism, when it comes to ideation.

So the speculator may actually bene#t the market? Australian economist Israeli Kirzner introduced the notion of pure discovery and the entrepreneurial function of the market (Ebeling, 2001). Kirzner argues that it is the entrepreneur plays an important role in letting the market "nd an equilibrium by "nding ‘errors’ that imply room for arbitrage. &us it is the speculator who makes the market function. Kirzner ague that it is the alertness of the speculator that makes sure the market can function as an information aggregation mechanism such as Hayek argued. &us the opportunity from pro"ting from an undiscovered potential. Subject to the share of participants who invest in the ideas for what they actually are and those who invest on presumptions about the rationality of other participants, the idea market can have the bene"t of disco#ery (Ibid), because the market continually rewards those who discover value before others. &us there is an incentive to keep monitoring the other participant’s ideas and add knowledge and scope via comment and "nally garner attention by means of investing in ideas.

Learning and outcome $om Case 1: Case one was linked to the potentially detrimental proposition, that a type of speculation linked to the Keynesian Beauty Contest might set in. &e worry was that exaggerated focus on speculation in what others would judge to be a good idea, would lead to a loss in locally knowledge going in to the idea market evaluation of the ideas and suggestions. As was seen, the share of Speculators ranged from 20%-40%. Interestingly, the share of speculators in the three cases had a positive correlation to the relative value of the reward for being the best trader.

&is "nding suggests that one should attempt to "nd a balanced approach to rewarding the idea-traders in idea

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markets. Conversely, one might expect that greater rewards would draw in even more participants, letting us use the collective wisdom of even larger crowds. &is is a subject that will be discussed further, as a framework for implementation of idea markets is proposed, towards the end of the thesis.

Satisfaction

Looking at the table below, data on prizes, motivation and satisfaction. It is interesting to note that the level of activity is not directly related to the value of the prizes at stake in the competition. &e "rst case - Chemical Co. - represents the lowest level of satisfaction with the top ideas in the idea market, coupled with the highest value of prizes. However, a minority of the traders responded that they competed to win one of the prizes. &e winner of the competition for idea traders echoes this sentiment, as he reported that it was the element of competition the motivated him - not the prizes.

Prizes and Statements Chemical Co. Pump Co. Pharmaceutical Co.Prizes for top ideas Gi" worth 12000

DKKGi" worth 2000

DKKGi" worth 5.000

DKKPrizes for top traders Gi" worth 12000

DKKGi" worth 1500

DKKToken prize for top 10

Invited participants 392 250 95

Active participants 159 79 92

I wanted to win one of the prizes 14% (34% neu.) 61% 53%&e ideas with highest share value are the best 18% (46% neu.) 79% 71%I general, people have spoken positively about

the competition87% 67% 100%

I brought shares in ideas i genuinely believed in

85% 67% 92%

I invested in the ideas I wanted the company to act on

79% 65% 94%

Table 12: Motivation for participation and investment in ideas

While the small sample may not fully substantiate this, I propose that a key aspect of setting up and running an idea market is the ability to balance the motivation to participate and the rewarded behavior. &erefore, prizes for best performance as idea trader, should probably be restricted as not to motivate too much speculation. Interestingly, of the 35 Chemical Co. case respondents, 18 indicated in the qualitative input that they felt the market was more focused "nding winning traders than winning ideas. In the other two cases, this number was 4 and 3, respectively. If the lower value of prizes can push the perception of the idea market towards a voting mechanism for idealist players, the tool encapsulates two aggregation methods, direct democracy and market scoring rule.

Another type of collective intelligence

From the interview with the winning participant, a Speculator, it became clear that the speculation was reminiscent of Keynes’ description of level-1 players in a Beauty Contest scenario. In other words, they attempted

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to predict what the Idealists would perceive to be a good idea. Intuitively, this would seem to be detrimental to the overall goal of setting up the idea market. Somewhat surprisingly, it was found have a positive e%ect to the e$ciency of the idea market. &e positive e%ect can be ascribed to the so-called ‘Google-e%ect’. Simply put, users of Google will most o(en click on one of the top 5 results of their search, thus reinforcing the popularity of these pages. &e same e%ect made itself apparent when Speculators trawled through the numerous new ideas and invested in those they assumed others would like too. In the idea markets, ideas are listed according to their ‘share price’. By investing in these ideas, the Speculator would bring the ideas to the "rst page of the ‘ideas-section’, thus raising the probability that other participants will see the idea and invest in it. With regards to collective intelligence, this is signi"cant, becuse the type of interaction and use of the ‘ranking’ of ideas point to another type of aggregation of collective intelligence, implicit selection such as seen in recommender systems like Google.

Interestingly, there were a number of these type of Speculators. Inherently there will be a competition between the Speculators to spot the potentially most popular ideas early on in the competition. Essentially the Speculators will serve as a "lter for the remaining majority of participants, helping the overall market become more e$cient.

CASE 2 : MULTINATIONAL PUMP CO. - COLLECTIVE INTELLIGENCE?

&e second case covers another implementation of Nosco’s idea market, Idea Exchange, this time in the Danish division of a multinational company producing pumps and heat transfer devices. &e company employs more than 10.000 people around the world, with main o$ces in Sweden, Denmark, India and USA. Employees had little or no experience with innovation initiatives such as suggestion-boxes.

&e idea market was presented as an idea competition, where the categories were aligned with the company’s overall strategy. &e categories prompted users to come up with ideas for new products and processes for the company. &e most popular ideas and the best idea traders were rewarded with prizes. Compared to case 1, &e Chemical Co., the prizes were relatively inexpensive. &e survey was sent to all 79 participants and 22 chose to answer.

With this second case the aim is to explore aspects of idea markets that can help understand the markets and the role they can play in the knowledge creation process. &e interest is in understanding how individuals perceive the participation in an idea market. &rough the process lens and the notion of ideas as social objects, the development of the winning idea in the case is followed. &e implicit question is: Are the idea markets really ‘acts’ of collective intelligence and are they conducive to innovation? &e case is presented as a narrative, where one idea is tracked from birth to the end of the idea competition, while using Nonaka & Takeuchi’s SECI model to give some perspective as to how the progress can be understood in terms of knowledge management.

Nonaka’s SECI model is seen "tting, because it is the proponent of the collaborative culture, that was deemed to be one of the sources of the Japanese competitiveness in the 80’s (Chatti et al. 2007). However, the reader might ask,

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if the term collaborative and collective are equivalent. I will argue that a collaborative act, that is explicated into a document such as one one "nds in Wikipedia, or indeed a ‘winning’ idea that has been enriched by a number of separate, independent pieces of knowledge or information is a result of collective intelligence, that would not have been accessible in the same quality otherwise.

!e Idea

&e winning suggestion, who’s life in the idea market, was presented with a headline simply called “Simple Pump Unit”. &e person suggesting the idea, Simon, had been working in sales of &e Pump Co. for roughly 12 years. Simon was interviewed as a part of the case study. When asked about the origins of his winning idea, he reponded:

“I had this for some years, it was basically a cheaper pump than the one we were selling...As a salesman in the company, I had heard of the need for this many times. ”.

&is also correlates well with the fact that roughly 75% of the ideas shared in this idea market, and 40% the other two instances of the idea market were ones that the participants had been keeping in their heads or drawers for some time before the actual competition. &us, in the SECI lens, an idea, stemming from socialization.

Statement Chemical Co. Pump Co. Pharmaceutical Co.

‘I had some ideas lying around in my drawer already’ 42% 74% 36%

‘I was inspired by the ideas that were in Idea Exchange already’

24% 3% 6%

‘I got new ideas from the categories in Idea Exchange’

16% 3% 19%

Table 13: From the questionnaire: Inspiration for ideas in Idea Exchange

Socialization

Socialization is an important aspect of the knowledge creation for individuals and in groups. In classic epistemic thought, o(en exempli"ed in the cartesian school, there exists a dichotomy between the knower and the known. Nonaka & Takeuchi take up the notion of tacit knowledge from Polyani (1966), and inherently the idea of knowledge becomes more than objective truth - knowledge in the positivistic sense. &e socialization process, is signi"ed by the use of mental models that may not be directly possible to share with other in certain situations. Socialization in the process of knowledge creation is the result of experiences, and as such it is a subject in the "eld of the psyche and the senses. It can be di$cult to communicate these experiences, unless talking to someone who shared that experience with us.

With regards to innovation, socialization is important because it can give an insight into the needs and processes

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that occur for the customer, for product and position innovation, as well as the processes inside the company for process innovation. &e notion of empathic design (Leonard and Rayport 1997) has gained prominence. in the last decade, and is an example of how socialization is being used directly in innovation e%orts.

Externalization

Returning to the suggestion about cheaper pumps: When it has been shared in the idea market, it is externalized and thus no longer a tacitly held idea - it has become explicit. Needless to say, if the person had written the suggestion down and not shared it yet, it is still explicit. However, once the suggestion is formally introduced to the crowd in the market, it has been made explicit. As the SECI model emphasizes the conversion between the knowledge types, one should look at the conversion from the persons social interactions with customers to idea shared in the idea market. &er person who shared the idea, comes from the sales department of the Pump Co., and thus he may be considered to be the ‘senses’, the eyes and ears of the company. He states:

“I’ve been in contact with customers for more than ten years. Of course, not everyone has the same needs, but it’s safe to say that I have been getting a picture of what the di$erent types of customers want and where we lose to the competitors” (From Interview 1)

In the description of the idea, the author states that the company is well known for producing the best quality pumps, but that there are new markets opening up, where there is a need for more simple products. A(er this statement, the author poses a number of technical issues, that he asks the fellow idea market participants to comment on, and thus develop the suggestion. &is allowed for contribution from the crowd by not only letting them evaluate the idea, but also elaborate on the proposed idea and ‘"ll in the blanks’.

Combination

By leaving an idea open, thus allowing the other participants to contribute, means activating the combination phase. Remembering the innovation de"nitions o%ered by Beassant (2006), the 4 P’s, it is seen that the suggestion for a cheaper pump relates to Product Innovation, but also Position. If &e Pump Co. are to release a cheaper type of pump, this will also imply a strategic move as they go down in market.

Four people commented on the idea. Two did so in terms of technical speci"cations, one wrote to show her support for the suggestion and "nally one commented on the strategic implication of putting a cheaper device in a new market. &e idea was shared in the idea market for about three weeks. In that time, it gained steady popularity, residing at the top spot of the idea competition for the last two weeks. One of the key aspects of the idea market, is how it gives participants to contribute their support for an idea, based on ‘hunches’, tacitly held knowledge that they cannot or do not make explicit, yet goes into the support of a given idea. &us, the tacit knowledge that may into the collective judgement of an idea, may not necessarily become explicit in itself, however the collective judgement does become explicit - in the share price for that given idea in the market.

Internalization 67

Coming full circle in the spiraling, reiterative SECI model, is the internalization phase. &e "rst thing worth noting is the #ow that is created. 14% of the respondents indicate that they were inspired by the ideas, to come up with new ideas. &e number of such ideas in the case with the Multinational Shipping Co. was at 24%. &e internalization phase of the cycle is also where articulated frameworks are ‘taken to heart’, where the knower and the knowledge can no longer be understood as separate entities.

Based upon the SECI model, seen below, it is argued that it is the Externalization- and Combination phases, and crucially the conversion process between the two, where the idea market tool has clear merits, when looking at the structure of the communication. Internalization, on the other hand, is important because Nonaka’s epistemology (1995) does not separate the knower and knowledge.

Figure 11: Nonakaʼs SECI model with added points of idea market activity and functionality. Source Nonaka & Konno, 1998.

Collective Intelligence&e combination-phase of the SECI cycle, represents a potential for collective intelligence. &ere are three aspects of idea interaction, in the idea market. Firstly, there is sharing ones idea. Aside from this, there are two types where one can look for collective intelligence; commenting on ideas (developing) and investing in them (judging).

By having one’s idea dispersed to all the participants in the idea market, initiates a situation that allows re)ective (dialogic) collective intelligence to play out. &e bene"ts of this type of intelligence is that it allows the participants to “&nd and share information, critique logic and assumptions, explore implications, create solutions and

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mental models together. !eir diversity, used well, helps them overcome blind spots[.......] and develop better outcomes than they could alone.” (Por, 2006). Por’s description "ts well with the events in this second instance of the idea market. &e structure and nature of the communication surrounding Simon’s shared idea resembles that of re)ective collective intelligence. &e participants used their ability to add to the idea by commenting on it. When Simon shared the idea, it was based upon his personal observations from his experiences with sales.

&e idea was shared, and in the description, Simon opened for dialogue regarding some of the blind spots of the idea.

“I know that I’m not a scientist or in any way a technician. [.......]I decided that the idea might get even better and attain more in#estments, if I urged my colleagues to contribute. I Actually asked a lot of questions in the description. ”

&e questions were answered with two of the comments, and this helped qualify the idea even more. It can be inferred that this is what the crowd perceived, since the support for the idea steadily grew as the comments were added. In the end, Simon’s idea had amassed a share price of 40, meaning that 40% of the liquidity in the idea market had been invested in that the suggestion for ‘Simple Pump Unit’.

&e SECI model follows knowledge conversion, from observation to realization and articulation. At the same time, the #ow of information moves from mind to person to group, and back again as the individual processes the signals coming from the collective.

&e internalization phase one that will be examined closer in Case 3, &e Pharmaceutical Co., where the the ‘Ba’ concept will be examined. &e notion of ‘Ba’ encompasses phenomenology and transcendence and has logical linkages to the understanding of the internalization phase in a broader context. !

Learning and outcome $om Case 2: &e winning idea has been followed from when it was devised, shared, enriched and evaluated in the idea market. &rough the lens of the SECI model, a winning idea coming from one person - through Internalization and Socialization - and Externalized though it being #oated in the idea market was followed. A(er the Externalization of the idea, is was subject to Socialization and Combination as other participants were using their comments to develop and evaluate the initially shared idea.

By opening up to this mix of Socialization and Combination is can be inferred that the information architecture in idea markets does enable a type of collective intelligence. Dialogic collective intelligence, and Relevational collective intelligence are the types that resemble the process the most. Both these types of collective intelligence are signi"ed by a structure of many-to-many communication. &is structure of allows members of the group to contribute their exact knowledge where they can, while also engaging in communication that may reveal some of their ‘dark-spots’ - their non-knowledge, which was already available somewhere in the company. From the viewpoint of the company, one might talk about the collective brain "nding new connections between the neurons

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and becoming aware of knowledge, information and abilities that were undiscovered.

CASE 3 : PHARMACEUTICAL CO - THE IDEA MARKETS AS BA

&e third case focuses on how an idea market can be understood in terms of Nonaka & Konno’s concept of ‘Ba’ and "nally Dick Stenmark’s check-list for creative systems. If an idea can be conceptualized as a Ba - What can then be said of such a Ba? As was seen in the literature review, the Ba concept lends itself well to the examination and understanding of idea markets. Firstly, it lies in direct continuation of our chosen epistemic approach, the SECI model, where knowledge generation takes place in conversions between tacit and explicit knowledge. Furthermore, the authors posit that a ‘successful’ Ba encompasses room for creativity and emergence - two factors that are assumed to be fundamental in innovation in organizations. &erefore, it provides a point of departure when setting the framework for a successful implementation of an idea market. Finally, the terms upon which Ba can be evaluated are largely qualitative, which corresponds well with the qualitative methodology.

&e company in case, is a Danish division of an international pharmaceutical company, where roughly 100 people work, comprising of scientists, marketing, sales and operational sta%. &e idea market was implemented as an idea competition which went on for two weeks. &is speci"c instance of an idea market is very "tting when examining Ba, because the company went to great lengths to put the idea competition in a frame or context where the participants attained a mutual understanding of what the idea market was to achieve. Nearly all the employees were present at a "eld trip. &e "eld trip allowed the employees to see their products being put to use in the real world, and get to talk to users and buyers of their products. A(er the "eld trip, the group was gathered for a talk about innovation and "nally Nosco’s idea market platform, Idea Exchange, was introduced.

&e "rst case was used to illustrate behavior in idea markets. - especially in relation to the Beauty Contest notion, as this was assumed to be detrimental towards creativity in groups and ‘crowd wisdom’. Secondly, the idea markets were investigated in terms of collective intelligence. In this third case, idea markets are observed through the lens of the chosen lens, Nonaka & Takeuchi’s SECI model and their adhering ‘concept of Ba’. As stated, the reasoning behind choosing the SECI model and especially the concept of Ba, is our implicit view that creativity is a founding principle of innovation and therefore important for us.

It is asserted, there is a logical link between the concept of Ba and some of the fundamental phenomenological tenets of collective intelligence. Most notably the links between self and transcendence. Nonaka & Konno state that Ba can be physical, virtual, mental or combination of the two. Seeing as idea markets are essentially a piece of so(ware allowing for a group of people to interact online, it is certainly a virtual Ba. However, the element of competition, group creativity and "nally the expectation that the key to emergence takes place at the micro, interpersonal level, means that it is also interesting to understand how an idea market is perceived as a mental Ba.

In the survey, this is speci"cally addressed by the ‘so(er’ statements that respondents are prompted. &e questions 70

are asked to determine the number of participants who enjoyed the initiative, the share of participants who would participate again and the share of participant who felt that the initiative generated value. Of the 92 active participants,46 persons replied to the survey, and as can be seen in the table below, the reception of the idea market was the most positive of all three cases.

Statement Multinational Shipping Co.

Multinational Pump Co.

Multinational Pharmaceutical Co.

I enjoyed myself during the competition

94% 90% 98%

I would participate again 97% 95% 100%

People have spoken positively about the competition

87% 67% 100%

I learned something new about my work in the process

70% 89% 95%

&e ideas with highest sharevalue are the best

29% 18% 71%*

Table 14: The usersʼ overall experiences with the idea market.

Originating Ba, Socialization

Originating Ba is the existential dimension is the existential dimension to idea markets. Originating is Ba is

the context where socialization takes place. A good example of how the implemented idea market in case 3

had an effect on the originating Ba came in the interviews with participants. In the in surveys and

interviews - in all three cases in fact - it became clear that the idea markets and more importantly the ideas

themselves became social objects or memes in the company. In my double-role as project manager and

masters student at Copenhagen Business School I had the opportunity to enact the Action Research

paradigm by attending lunch at the cantinas of the three companies. From the conversations with

employees it became clear that the ideas and idea markets had become the subject of many “water-cooler”

conversations and lunch conversations. The ideas had made the move into the social sphere of the

company.

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Figure 13: Activity in the idea market. (Source: Idea Exchangeʼs Stat Module)

Looking at the hourly distribution of activity in the idea market, the sentiment above is reflected. Activity

peaks at and around lunch-time. From the conversations during lunch time, it became clear that the subject

of ideas had often replaced other social subjects such as last nights television programs. It is also interesting

to note that 22% of the activity took place outside of working-hours at The Pharmaceutical Co.

In regards to originating Ba, these numbers and the observations made during visits with the company are

significant because moving the conversations away from the cyber Ba means that ideas are debated in an

environment of face to face interaction, a more nuanced type of communication where body language,

intimacy and tacit knowledge can be communicated. The fact that everybody at The Pharmaceutical Co.

was involved meant that the idea market became a common frame of reference.

Interacting Ba & Cyber Ba, Externalization and Combination.

&e &e externalization and combination phase is where the e%ects of the networked nature and the market mechanism of the idea market is most pronounced. In fact an idea market in can be thought of as a cyber Ba wherein tacit knowledge is explicated. However, tacit knowledge will most likely go into a number of the decisions made to invest in certain ideas, thus bringing them to attention for other participants. Nonaka & Konno (1998, p. 47) mention how information technology can bene"t and enhance the knowledge conversion process.

It is a deliberate choice that Interacting Ba and Cyber Ba have been combined as one Ba. I argue that this is one of the e%ects of the tools as they make their way in to companies under the name Enterprise 2.0 tool. Of the common features in these tools is that all users have their individual pro"les, and thus face-to-face interaction is to some extent mimicked in these networks.

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When looking through the comments of the ideas that have been observed, some qualify as dialogue between the individuals organizing around the idea. Others do more of a job speaking to the idea, the combination of already explicit knowledge and information. As the lines between the the tacit-explicit conversion and the combination of explicit-explicit knowledge are blurred one also attains a less formal type of communication in the idea markets. &e fact that Nosco’s idea market platform gives the participant the option to comment and share ideas anonymously may also speak to this argument.

Basho, Inno!ation & Complexity&e Ba concept is related to transcendence wether it is from person to group, group to organisation or organisation to the surrounding environment, the market. In the de"nition of collective intelligence as collective consciousness &e aspect of the surrounding environment being part of the Ba of the company, is interesting. &e element of transcendence applies to not only the individual in the group or the group in the organisation. It also refers to the organisation or company in the market. &us, the element of transcendence in the Ba concept, Basho, has equivalent terms in marketing literature. In “Wired to Care”, Dev Patniak (2008) “tells the story of how companies prosper when they stop worrying about their own problems and start caring about ordinary people” The findings

include a direct correlation between companies’ success and ability to be emphatic with their customers. The same sentiment is seen in Leonard and Rayport’s (1997) “Spark Innovation Through Empathic

Design”, where latent customer-needs are observed and acted by means of user observation and user centric design.

Innovation-wise, Cheborough’s “Open Innovation” points to this advantage and the understading of Basho, can also point us back to Peter F. Drucker’s quote at the very beginning of this text. Getting idea markets right for the internal organistion of the company, is a way of doing things right however, if we are not doing the right things, the idea markets may be of lesser strategic value for companies. When building and open an idea market, a cross between a a dialoguing Ba and Cyber Ba is initiated, that exists in the social Ba of the organization which exists in an is dependent upon the outside world, the environment. &is relation is illustrated below.

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Figure 14: An interpretation of the inner and outer dynamics of the SECI model and Ba for the individual, group, organisation and surround world. Source, Nonaka & Konno and authorʼs reflections.

&e openness and ability to disseminate ideas and combine it with dispersed knowledge, information and creativity from the groups of people participating with organizations may lead us to induce that the cyber Ba brings us closer to the ‘real world’ outside the company, the market. So could the idea market be moved even closer to the market?

One of the principles of Ba that Nonaka, Tomoya & Konno (2001) underline that a Ba either emerges spontaneously from interaction, or can be constructed to the degree where one is able to decide the mix of people involved in the Ba, the nature of their interaction and level of autonomy. &us it might be inferred that there is strategic opportunity to involve either open innovation partners or customers and users. As such one might talk of structural organizing principle of collective intelligence when addressing the interactions that take place with in the organization, as seen in the three cases.

Learning and outcome $om Case 3: From case 3, the idea market at &e Pharmaceutical Co. was observed through the lens of the Ba concept. By doing this meant going further than the SECI model where the knowledge conversion processes taking place around the winning idea in the idea market were observed. In the third case, the study was more attentive to the context of the communication

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Case 3 was arguably the most successful implementation of an idea market. As was seen, &e Pharmaceutical Co. went to great lengths to ensure that the group as a whole would experience the actual application of their products at a hospital.

FINDINGS

Case 1 and Proposition 1: !e market mechanism may prove detrimental to the overall purpose.

&e implementations of Nosco’s idea market platform, Idea Exchange, were explored as cases to give answers to the propositions. In the "rst case, the e%ects of the market mechanism were our subject of interest. It was quite clear, that the market was an e$cient way of organizing the inputs based on creativity, knowledge and information from large groups (Hayek, 1945). But is the method also e%ective? &e theoretical underpinning of this question was Keynes’ notion of markets having a tendency to become Beauty Contests, which happens when people are rewarded for predicting the tastes and choices of rest of the group, rather than having their own opinion. &e surveys in the cases showed that there was indeed participants in the idea market, who would use technical analysis of the ideas they would invest in. &ese were termed ‘Speculators’. In endogenous markets, these speculators can pro"t from following beauty contest behavior (Keynes, 1936). Aside from the the speculators there were the’Idealists’, who would rate ideas on the basis of a fundamental analysis (Shiller, 2001).

It was found, that the Idealists outnumbered the Speculators in all three cases. In the interviews and surveys, it also became clear that many would mix the two strategies. An interesting lesson from looking at strategies of the two groups was the relation between the two.

&e interplay between the Speculator and the Idealists showed that the former group could pro"t greatly from spotting an idea before it had made it up to the "rst page in the leader board of suggestions and ideas. &is helped the idea to attention from the larger Idealist group, and this helped the Speculator make a pro"t in the idea market. &is mimics the positive feedback mechanism principle of Page Rank in the Google search-engine. &e “Google-E%ect” took place, presumably because many users will only look, and click, at the "rst page of search-results, thus helping pages with a higher Page Rank stay on top. In his e%orts to win, the Speculator would have bring forward under-recognized ideas that held greater potential than others. &us the speculator has the role of the entrepreneur in the market. Kirzner (Ebeling, 2001) would hold the the speculator is the hero of the market system. &is is the agent who discovers potentials for pro"ts, known as ‘entrepreneurial alertness’ in Kirzner. From this, it is proposed that idea markets encompass two of aggregation mechanisms mentioned in the section on collective intelligence: &e market mechanism and the implicit selection mechanism. It is also concluded that while there is a risk that the rationality of Keynesian Beauty Contest can set in, this has not been seen the three cases. Rather, the speculator pro"ts comes from

Case 2 and Proposition 2: Idea markets are an actual representation of collective intelligence.

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In the second case, the attention of the study was turned at how an idea was created, enriched and evaluated in the idea market. For this, the ‘life cycle’ of an idea throughout an idea competition in the idea market was followed. Following to Nonaka & Takeuchi’s SECI model, a winning idea was followed. Coming from one person - through Internalization and Socialization - and Externalized though to the idea being #oated in the idea market. &e SECI model dealt with the conversion processes, and a(er the Externalization of the idea, is was subject to Socialization and Combination as other participants were using their comments to develop and change the initially shared idea. By opening up to this mix of Socialization and Combination one could infer that the information architecture in idea markets does enable collective intelligence. Especially the types of collective intelligence called Dialogic collective intelligence, and Relevational collective intelligence, both these types of collective intelligence are denoted by the way in which structure of many-to-many communication is facilitated. &is structure of allows members of the group to contribute their exact knowledge where they can, while also engaging in communication that may reveal some of their ‘dark-spots’ - their non-knowledge, which was already available somewhere in the group. From the viewpoint of the organisation, one might talk about the collective brain "nding new connections between the neurons and becoming aware of knowledge, information and abilities that were undiscovered.

Case 3 and Proposition 3: Idea markets are suitable as innovation platforms.

While it may be impossible to objectively prove the existence of collective intelligence and the successful Ba, the qualitative approach general qualitative approach gave some assuring indications, that can help make the argument, that case 3 was an instance of a successful Ba, and collective intelligence. As Nonaka & Konno write, (1998) culture and Ba, are interlinked. &ey write about Honda and 3M : “Here, interacting Ba for collective re#ection are institutionalized in the company culture” (1998, p. 47) When understanding Ba in terms of the common context that humans interact in, it seems logical that culture plays an important role. &e reason that case 3 was somewhat more successful than the two former cases may lie in the way it was set up, and it may lie in the culture of the Pharmaceutical Co. But there is no conclusive evidence of this. On the other hand one may argue that idea markets can be seen as a tool for pushing organizational culture in a direction that facilitates collective intelligence.

&e concept of Ba as the environment for knowledge creation and thus idea markets for knowledge creation is subjected to a "nal review before moving on to the perspectives and conclusions about the idea markets. Nonaka & Konno (1998) state that there are a number of ‘necessary conditions’ to energize the SECI process taking place in the Ba context. &ese are autonomy, creative chaos, redundancy, requisite variety, love, care, trust commitment. While it has been found that commitment is one of the main drivers for possibly perpetuating an idea market as a successful Ba the others will have gone relatively unnoticed in the written pages so far. &e conditions are similar to those of Stenmark, and his ‘check-list’ has been chosen for its direct applicability to information systems such as idea markets. &e eight points, seen in table below has been answered via the experiences and data collection from the the three cases.

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FACTOR RELEVANCE FOR IDEA MARKETS

IN IDEA MARKETS?

Non-preconception principle Rigid, linear processes are commonly referred to as one of the most detrimental factors for creativity and inno!ation. Such processes are a result of preconception. In idea markets there may be categories or themes, if these are too sharply formulated, it may an example of preconception. However, this relies on the context of the idea market, not he market itself.

#e non-preconception principle relies to large degree on company culture. #at being said, we can control how idea markets are framed, and thus control the direction of the collective group. Furthermore, opening up for all to participate means that knowledge may come from unexpected persons in the company.

Skunk works / Heterarchy Emergence tightly linked to the idea of ‘skunk works’. In skunk works, the hierarchy is suspended and employees are allowed to organize around ideas and projects, heterarchially.

Yes. In all cases, it was seen that employees organized around and supported ideas they were passionate about. An example is the group who brought forward an idea that had been scrapped years before.

Serendipity One of the underpinnings of inno!ation in the organisational setting is the tradeo" or the balance between ‘creativity’ and ‘control’. While we cannot control, or foresee many creative acts, we can give rise to the probability that a ‘happy accident’ occurs.

Yes. #e winning idea in case 2 is an example of how an idea can be enriched from unexpected sources.

Diverse stimuli By letting members form many areas in the company partake in idea markets, we seek for each idea and each participant to receive feedback #om many points of view.

Yes. Diverse stimuli comes from the mix of people with di!erent backgrounds, participating in the idea market. Furthermore, the dialogue between employees in the system can help open up to new viewpoints.

Within company communication Information should be let out of the ‘silos’ of many corporations, because ideas can be greatly enriched by cross-pollination #om di"erent points of view.

Yes. Within company communication is reinforced by the networked structure and informal nature of the idea market.

Reciprocity By reciprocity, Stenmark means the person to person relations in the intranet. In idea markets, everyone has a pro$le and the same possible impact on the market. %us we assume there is a high level of reciprocity. %ere may be factors inhibiting reciprocity, such as ‘real life’ status outside the market. %is may hinder reciprocity.

Yes. Each user has the same potential e!ect on the market. Furthermore, users have their own pro"les. #e organizational setting of a company also means that participants have a shared future goal. Success for the company.

Motivation While the extrinsic motivational factors are clearly evident in the rewards and possibly recognition. However do people actually want to participate? Are prizes necessary?

Yes. Both types of motivation are present in idea markets. #e rewards and recognition represent extrinsic motivation. A majority of users respond that they ‘enjoyed’ participating - a sign of intrinsic motivation.

Rich information provision %e latest and most popular ideas are presented to the users upon logging in the web based idea market. %e ability to post link #om the World Wide Web means that there is a multitude of information presented and available in idea markets

Yes. Possibly all web applications provide rich information provision, due to their hyperlinked nature. However, the data presented to the user also provides information about latest and most popular ideas right now.

Table 15: How did the idea market fare, according to the check-list for a creative intranet

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&e response to the third proposition is that idea markets can indeed serve as a catalyst for knowledge sharing and creativity. As seen in the table above, idea markets can comply with all eight factors in the innovation tool check-list. In relation to the third proposition, it might also be "tting to reiterate the message sent from the Senior Director of Business Development at 'uallcom, Ricardo dos Santos; “A PM or other collective intelligence approach to the "ont end of inno#ation (creation/selection) is game changing and has several advantages, most notably being able to handle a broad scope of topics and high number of participants (without collective intelligence, and if dependent on traditional committees for decision making, you have to compromise either broad scope or high number of participants ” News Group about prediction markets. PMIA.

CHAPTER 6 : PERPECTIVES

&e "ndings from the samples of three companies can be divided along two lines. One is the instrumental aspect of the idea market which relates to how the process of idea sharing, development and selection takes place. &e other line in which "ndings can be categories is less tangible, dealing with the social dimension of the interactions in the idea markets, this is the part of collective intelligence that relates to collective consciousness. On one hand the SECI model, Stenmark’s check-list and the concept of social objects were used to research the instrumental aspect, secondly the concept of Ba and was introduced and applied understand the e%ects of the idea market. To classify the "ndings, a Cartesian split of mind and body is used. &is dualism captures the two sides of knowledge (tacit and explicit), knowledge creating contexts (existential and systems), collective intelligence (distributed intelligence and collective consciousness) as well as organizational principle and the e%ect of the tool, the idea market.

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Idea Markets

Ability to network around Social

Objects

Social Interaction

Systemizing Ba

Existential Ba

Tacit

Explicit

Distributed Intelligence

Collective Consciousness

Inner Organisational

Coherence

Environmental Strategic Alignment

Efficiency(Acting)

Effectiveness(Sensing)

(Body)

(Mind)

Interaction Dimension

OntologicalDimension

Epistemological Dimension

Collective Intelligence

OrganisationalPrinciple

Effect / Outcome

Figure 15: An integrative view, and a Cartesian split figure of how idea markets ideally affect a companyʼs innovation capabilities.

&e idea market in an ideal implementation will impact the organization on two levels. &e two dimensions can be understood in the dualistic terms of a cartesian split. &us, there are respectively the e%ect on body and mind in the organisation.

&e ‘mind’ path points to the more cultural aspects of the idea market. &e fact that the wide array of workers have been tasked with a decision in the search/select phase of innovation may have long term e%ects, in terms of context or Ba that are not seen at the time of observation and research. &e act of decentralized decision making may have further implications. In the surveys and interviews respondents indicated that they were positive towards using such a system again in the future, and that much of their perception of such an initiative would rest on the commitment to the ideas in the recently closed idea market. Here, in the article on Decentralized decision Making, it says : “Decentralized decision making also contributes to the core knowledge of group intelligence and crowd wisdom, o(en in a subconscious way.” (May"eld, 2007)

Peter F. Drucker highlighted the importance of decentralization, as “the key to the corporation’s success” (Heller, 2001). I conclude that, given the collective intelligence e%ects observed in the three cases, that idea markets provide a platform for decentralized decision-making in a crucial part of management, innovation-management. At the same time, allowing for decentralized decision making in the search select phase of innovation will represent a management innovation (Hamel, 2007).

A further implication is how this level decentralization is attained, and this points to a number of factors that rest on decisions outside the idea market. &e most important has been found to be commitment. &e ability to act on the ideas "ltered through the idea market process is important for the recognition of the individual worker and the

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avoidance of cynicism setting in. Another motivational factor is reward schemes for participants. While the promise of a substantial prize may induce more participant to join, the side e%ect may well be more participants speculating and hence more general skepticism towards the eligibility of the top ranking ideas in the idea market. &e fact that the case with the least valuable rewards also showed a remarkably high level of satisfaction with the top ideas, point to this. &eoretically it has been suggested that idea markets may cause a change in the epistemic and ontological properties of the knowledge creation process. Tacit knowledge can now become a part of the decision, and thus go in to the innovation process, without necessarily being formalized. On a practical level, such a change will encompass a change in roles and collaboration, which are probably the most important elements of the enterprise 2.0 ethos.

Crowdsourcing by the means of an idea market can help lessen the importance of positivistic and inductive justi"cation of idea processing, leading to a more intuitive method where tacit knowledge can #ow into the decision making in innovation management thus paving the way for abductive reasoning which is thought to lead to more radical and disruptive innovation. At the same time, the market as an aggregation mechanism can lead more e$cient knowledge management and decentralized innovation in and around organizations.

Limitations of the Study and Suggestions for Further Research

!&e main limitation to the study has been the access to samples. While the access to three idea markets has yielded great amount of data has lead to insights about the markets, it would be interesting to study idea markets with other incentive structures than short term competitions.

Furthermore, in the long term it would be of interest to study idea markets with other types of focus than employee driven innovation. Such contexts could include longer term strategic forecasting, the “combination of strategic planning and forecasting” (Duus,, 2008), with focus on longer term market indicators. If a company was trying to "gure out, say what platform to develop for, Linux or Windows, harnessing the collective intelligence and the hunches of all employees and possibly other stakeholders would be interesting and of most likely of great interest to companies.

Much in this vein, it would be to follow idea markets in continuous use. Coupled with innovation strategies, idea markets involving stakeholders internal and external to the company can potentially create a process where the company and the surrounding environment is continuously scanned for opportunities.

For all the merits of the reception of the idea markets, it should not be forgotten that people essentially just enjoy brainstorming (Heslin, 2009), thus the idea markets should not be given credit alone for the positive reception among users. &us, a comparative analysis of two aggregation mechanisms for collective intelligence. Voting Mechanism vs. Market Mechanism. While the focus of the study has been to highlight the changes that enterprise

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2.0 tools such as idea markets bring about, a future study of the di%erent types of mechanisms would be expected to generate interesting results and value for potential users.

A good idea market and a good Ba

&e relations between the di%erent factors that comprise an idea market and a good Ba is illustrated below.

Figure 18: Interplay between the factors that affect idea markets. The authorsʼ own creation.

Inspired by James B. Reiley’s dynamic charts (2001) the chart describes the factors that are involved in the idea market. &e signs on the links signify the e%ect that the interlinked factors have on one another. &e kay factor from the cases has been commitment which is again linked to the most important factor of a proposed innovation model: Recognition. Also of great importance is motivation. As can be seen, one of the important features of leading an idea market is striking the balance between getting enough people to participate while keeping Speculator behavior in balance. An outline for a new innovation model is presented in the appendix.

CHAPTER 7 - CONCLUSION

“How can idea markets, as a mechanism for utilizing distributed intelligence, have an impact on innovation in business organizations, and what are the limitations of this approach?”

&e identi"ed problem in the thesis has been to determine how the use of idea markets can impact idea generation in organizations.

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Proposition 1: !e market mechanism will turn the market into a Keynesian Beauty Contest, undermining the bene&ts of distributed intelligence in the idea sharing and evaluation phase of the inno#ation process.

&e "rst part of the idea markets were subjected to study of the market mechanism in itself. &e assumption built upon Keynes’ notion of the Beauty Contest. If such a train of thought was dominant, one could expect ine$cient markets, and the use of the market mechanism might prove detrimental for the act of emergence during the sharing, development and selection of ideas. Two extremes of behavior were highlighted; the idealists, who saw their actions in the market as a way of voting and helping the ideas they genuinely believed in succeed in the market. On the other end of the scale was the Speculator, who did in "tted in to Keynes’ concept of Beauty Contests, but also Kirzner’s idea of the entrepreneur, who discovers opportunities for pro"t while actively letting the market "nd its equilibrium. It is this trader who gives the market a sense of alertness (Ebeling, 2001) and thus speed which is important for innovation (Bessant, 2005).

Case 1 has shown, that participants behave in ways that comply with the notion of Keynesian Beauty Contests. However, the mechanism may be limited to such a degree that the actions in the market can have a positive e%ect, towards out purpose of explicating as much knowledge and creativity as possible. &e internal nature of the implementations of the idea markets in companies, means that the e%ect is more that of a market-square (people marketing their own ideas, say) inside the company.

Proposition 2: !e process in idea markets constitutes the application of collective intelligence to inno#ation.

&e search for an understanding of the impact of idea markets has been based upon theories on innovation, markets, qualitative research action research, Nonaka’s SECI framework for knowledge and the notion of collective intelligence and ideas as a social object. Enlisting a crowd to enact their collective intelligence, can give access to collective genius, volatile mobs and anything in between. Once this has been overcome, there is the market itself. &e market can turn into a Beauty Contest and can lead to bubbles, but on the other hand, the market may the optimal for organizing knowledge around the collected ideas of a group of people. In fact the market may be understood as a space wherein meaningful social action and interaction can occur (Storr, 2008). Initially, it was asserted that idea markets mainly rely on the market mechanism for its aggregation of collective intelligence. What was interesting to note from the users was how implicit selection (listed in recommender systems in the collective intelligence typology) has played an important role in participants’ interactions around ideas in the system. As a result hereof, it is concluded that idea market are indeed collective intelligence applied to the innovation process.

Proposition 3: Idea markets are suited as an inno#ation management tool.

Finally viewing the idea markets though the lens of the Ba concept, allowed understand how the market "t in to the context of the individual and the organisation as a whole. Layard’s notion of the ‘good Ba’ also gives an

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indication of how to best implement the idea markets to heighten the chances of successful collective intelligence and innovational outcomes.

Idea markets have in the three cases proved to have their promises but also pitfalls. &e promise of a technology or platform such as idea markets is that one or more types of collective intelligence are activated, and judging from the anecdotes in the cases, one can involve people to give them a degree of ownership in the ideas they suggest, enrich and support. If an idea market is set up correctly, it may facilitate innovation. People interact with ideas by using knowledge and creativity. If the market part of the idea market is a catalyst (initiate a positive feedback) to keep people intrinsically motivated thus enhancing creativity, which is understood to be catalyst, initiating and maintaining innovation processes (Amabile, 1983), while building or enforcing a culture or climate suited for innovation, From an organizational stance, structures of collective intelligence can lead to organizations behaving as complex adaptive systems. One may speak of organization rigidities of a hierarchy being replaced by more adaptive heterarchy.

From a knowledge management point of view, the implementation of idea markets can lead to a structure of knowledge sharing and development where everyone is involved can share their knowledge and opinion across organizational silos. For the individual employee, an idea market can represent an enjoyable method of organizing around ideas. It was shown that intrinsic motivation was a driver of participation in idea markets. Amabile’s studies have shown that intrinsic motivation leads to greater creativity which is exactly what is sought to facilitate when considering the climate for innovation in the company.

Idea markets are both a marketspace and a marketplace. &ere is the potential of moving towards a decentralized mode of innovation with relatively simple means - a web application. If the element of the market can help create a Ba, a space for knowledge creation that is inherently intrinsically motivating to participate in, idea markets should #ourish greatly in the innovation economy. However, many other social enterprise tools are emerging. One of the more interesting possible future scenarios for research of idea markets would be to move on from the perspective of how idea markets are a novel solution and to begin comparing idea markets to other aggregation mechanisms such as voting systems and other collaborative innovation systems.

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idea markets -in the innovation engine. APPENDIX

FROM WISDOM OF THE CROWDS TOWARDS PASSION OF THE CROWDS:

PROPOSAL OF AN INNOVATION MODEL BASED ON COLLECTIVE

INTELLIGENCE

Inno!ation Management and Idea Markets

Following Hamel’s concept of management innovation, and on the background of the !ndings in the three, a new model of innovation could be envisioned. A hybrid between the linear, controlled Stage-Gate model and the decentralized collectively intelligent mechanisms of idea markets. What I propose is a model where the aggregation mechanism is used for decision support throughout the innovation process. "is would also make socialization part of the process, thus rendering the ideas as social objects. At the same time, the model is in oscillation between divergence and convergence of knowledge and information - the knowledge creation process. It can be argued that the Stage Gate model has the same feature, however the hierarchy is used to process said information, rather than the market mechanism and the implicit selection mechanism. "us I suggest a model of innovation management that synthesizes the use of idea markets and the stage gate model. Such a system would encompass the same phases as the stage gate, while avoiding some of the common problems such as bounded rationality by using decentralized knowledge creation.

Figure 16: The innovation process (Bessant), Innovation management (Cooper) and the proposal for a new innovation management process based on tools for aggregating collective intelligence.

A new model would make use of the collective aggregation mechanisms to make use of the dispersed local knowledge that the many stakeholders are understood to have. !e beginning of the system would be a forecasting market about the factors in the company’s environment that are of importance. Ideally industry experts and employees would participate in such a prediction market. From the predictions in such a market, a variety of idea markets can be imagined - for customers, employees, partners and other stakeholders. As ideas are moved forward in the pipeline it is important that they remain social objects- !erefore, accepted ideas should be moved forward into a new market where prices are not dependent on actions of the other users, but of the actual events in decision making and development.

During development, preference market could be applied as a focus group. Finally, as products, services or other changes are introduced to reality, the process could end in another forecasting market, predicting the success of said innovation.

At the same hand, a new model for innovation management should ideally include a less stringent, positivistic approach, as this can deter radical innovation and creativity. !e context or the Ba of the innovation management method should aim to be enjoyable and rewarding for the participants in

innovation. !is will also help the ideas that go into innovation become social objects, such as mentioned throughout the text. A new process of innovation management based on collective intelligence will also be equipped to handle or absorb and make use of higher degrees of complexity. !us, less stable and less predictable undertakings such as radical innovations become more feasible in a new innovation management framework. Itoh (1996) notes that:

• Abduction means reading between the lines.• Personal experiences and raw information are the basis of abduction.

• !e dissemination of knowledge and information including what is only peripheral, accelerates abduction.

• Abduction facilitates strategic thinking.• !e emotions that accelerate abduction are: exaltation, excitement, passion, expectation, trust,

con"dence and conviction.

• An atmosphere were people are free to say whatever the think, accelerates abduction.

• Rapid prototyping accelerates abduction.

Abductive thinking should be at the core of the proposed innovation model, because the goal is to spark creativity, include tacit knowledge and create passion.

Passion as metric

Prediction markets index predictions of events, but what about idea markets? Given that the sender of an idea market is able to create the conditions where participants are recognized for their ability to share and evaluate ideas and do so under terms where a majority of the participants behave as the idealistic trader, the indexed element may be the passion of the crowd for the ideas. For some companies such as focus could "t their HR strategy. In other companies it might prove more fruitful to make the idea market more event-driven such as seen in forecasting and news prediction markets. !e share prices of the ideas in markets dominated by idealists can act as a method to gauge the collective ‘passion’ for the ideas in the ideation phase. !e more passion, the more people will see the given idea because of implicit selection - !e Google E#ect. At the same time, large groups of people interacting around the social object will also raise the probability of the the serendipity and relevational collective intelligence.

Passion can also be related to Teresa Amabile’s notion of intrinsic motivation which in turn is proposed to lead to higher levels of creativity. !us, it could be expected that indicators of high levels of passion about certain ideas would lead higher concentrations of tacit knowledge, abductive reasoning and creativity being applied to those ideas.

Rethinking the wisdom of crowds

A new model of innovation with the included tools of harnessing collective intelligence should also seek to include di!erent sets of stakeholders. Aside from the inclusion and recognition of as many employees as possible, the idea markets could also be set up to include customers to come with suggestions, just as well as preference markets could be used instead of focus groups. As it was seen, the markets did function as an aggregation mechanism, however it must be questioned if wisdom of crowd was harnessed. Of course, some degree of knowledge sharing has been seen throughout the three cases. "e ideas that the collective deemed to be the best became social objects in the sense that employees organized around them to share information but also let their support for certain ideas be known. When the idea markets were interpreted through the lens of Ba, as a shared space for knowledge creation.

It was also clear, that the market mechanism could not mimic real life stock markets because of agent rationality, which tended more towards idealism rather than pro#t optimizing behavior. "us the price that the aggregated choices of the agents in the idea market are more of a marker of passion for the ideas. Why is passion important for innovation? According to Amabile (1986) intrinsic motivation is an important element for ful#lling the creative potential that lies in an organization. Gary Hamel’s “"e Future of Management” (2006) proposes an Innovation Management Framework 2.0, wherein passion is cited as the most important ‘ampli#er’ and ‘aggregator’ for e!ort towards innovation. Finally Nonaka & Konno (1998) argue that organizations that have the ability to engage the tacit knowledge of employees and create a shared space for knowledge creation that builds on trust and passion (love) have the potential of becoming innovation ‘hotbeds’. Based on this, I propose that aside from the wisdom of crowds, innovation oriented organizations stand to gain a lot from also considering the passion of crowds.

An emphasis on recognition

Both Nonaka (1998) and Stenmark (2006) point to recognition as being an important part of the Ba

and creative platforms for innovation is recognition in some way or the other. Nonaka states that the

conditions for a Ba the successfully energize the SECI knowledge creation process include, love,

care, commitment. Honneth (2005) argues that recognition is the basis for all social interaction.

Honneth sees the individual as intersubjective, thus humans understand themselves through

interactions with and recognition from others. Torrinton, Hall & Taylor (2004) cite recognition as

one the key factors in Human Resources Management. Idea markets are a priori an example of

recognizing behavior from the sender, basically saying “I’d like to hear what you have to say”. The

commitment to the ideas was deemed very important by the employees interviewed and surveyed.

This also represents a level of recognition of the idea market and the ideas shared therein.

Embrace complexity by crowd sourcing

A new model of innovation management allows for aggregation from different stakeholders at all

points in the process. Crowd sourcing mechanisms such as idea markets, forecasting markets, voting

systems and preference markets allow for rapid and cheap testing and execution, allowing constant

experimentation. The communication structure in the traditional stage-gate model reduces

complexity by means of hierarchy and the importance of empiric evidence as justification of

decision. While there a need for simplicity in innovation decision, for flow and speed, there may alot

of communication ‘lost in translation’ as decisions pass through hierarchies. Much of such lost

information could be thought to rest in it being:

a) Not empirically justifiable: The lack of hard evidence may hamper the innovations that are more

than incremental. Such innovations potentially entail disruptions in markets or radical innovation

in products, processes, positions and paradigms (Bessant, 2005). By using idea markets and

likewise aggregation mechanisms for idea generation can open up for ‘hunches‘ and intuition of

employees or other stakeholders in the innovation process.

b) Tacit knowledge : What could be called the communication that is ‘lost in translation’ can take

place when communication flows from between between group with differing functions. Thus

tacit knowledge embodied in persons cannot be included in stage gate meetings if they are not

present at said meetings. The share price of an idea in an idea market can possibly bridge this

gap by allowing employees or other stakeholders to communicate their support for ideas (based on

abduction, hunches, intuition) in the organization by investing in it.

Embrace chaos by crowd sourcing

Finally there is chaos that has set in to the communication sphere with Web 2.0 technologies just

there is chaos in financial markets, housing markets and in subsaharan states where there are no

markets. Such chaos happens in the outer edge of the Basho (Nonaka & Konno , 1998), where the

organization meets the market or environment. The music industry was faced with such chaos at the

turn of the millennium as digital distribution threatened the business model of selling IP on

compact discs at a premium. It took entrepreneurs to find new business models that could bridge the

gap between a new kind of technology, new consumers and the old music industry. Artists could also

embrace the chaos by giving their music away on sites like Myspace which has entailed success for a

number of artists. By entering collective intelligence, the employee and the company on a larger

scale in the final Basho, may transcend the self to absorb the surrounding chaos to make sense of

situations and scenarios.

Thus an innovation model building on collective intelligence tools wold represent an efficient and

effective (Drucker) of dealing with the complexity and chaos surrounding companies and other

organizations.

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Interview with participant behind the winning idea in case 2:The winning participant works in sales and marketing of the company, and has done so for roughly 12 years.

(Interview was carried out at the offices of the company in question in case 1. The interview was carried out in Danish, and transcribed in the same language. The passages that the writer has found to be of interest to the thesis have been translated)

(K) : Interviewer, Kristoffer(P) : Participant, Simon

K: First of all, Iʼd like to congratulate you on coming up with the winning idea, I also see you did quite well in the trader competition.

S: Yes, I think itʼs safe to say that I was quite involved in the competition.

K: First of all, tell me about how you perceived the whole thing in the beginning, when you where introduced to the competition. I mean the idea of subjecting your ideas to a system like this, a competition and a stock market?

S: Firstly, I was honored to be asked for my ideas. Iʼve worked here for more than 12 years, and Iʼve never been asked for my best ideas before.

K: And the stock market, the system?

S: It was funny. I have never traded stock in my whole life, so it was tricky to understand how the system worked in the beginning. But just as I was happy to be asked for my ideas, I was happy to look through my colleagues ideas, and isolate the best ones.

....It was really different, instead of just looking and saying, a thatʼs a good idea, you had tell youself: yes, but what is the best idea?

K: So did you follow the development of your own idea share?

S: Certainly, I mean you cannot spend too much time looking at the game, there is work to be done. But I did follow it. And my own profits from the shares i invested in. Sometimes I would check the market in the weekend. You know, it was fun.

...it was funny to see how people commented on my ideas, and people I didnʼt even know in Germany and Hong Kong wrote me to ask about my idea. As I said, it was a little honoring.

K: Did you leave any comments on the otherʼs ideas?

S: No actually I didnʼt. I spoke personally to one or two here in the offices about their ideas. that was it.

K: I noticed that you got about 15 comments on your own idea. You answered some of those?

S: Ah yes! People I didnʼt even know wrote to ask more specific questions and simply to cheer me on. I think it helped my idea along, maybe made it more specific and easier to understand. One or two comments were a little critical, but thatʼs what you get. The dialogue we had on the idea, for me was al little like marketing.

K: Alright, where did you get your idea from?

S: You know, Iʼve been here for so many years, and Iʼve discussed the need for this product with quite a few of our customers. But you know, normally new product come falling from the sky, and then you have to sell them. But this one has been in my head for quite some time.

K: How about comments? Did they improve the idea or were they more detrimental, putting it down?

S: I know that Iʼm not a scientist or in any way a technician. [.......]I decided that the idea might get even better and attain more investments, if I urged my colleagues to contribute. I Actually asked a lot of questions in the description.

K: Iʼve been told by the project manager that some users manipulated the system, in that they agreed to all invest in the idea that ended up in 4th spot. Can you tell me more about that?

S: True, I know a couple of those people. The idea that they put up was one that was tried 4 years ago, but got shut down. I think this was their window to show everyone that they still wanted to go ahead with the project, and they argument that with newer technology, it might be possible. I didnʼt invest, but it fine by me.

K: Tell me more about your strategy when it came to trading shares in your colleagues ideas.

S: I invested in ideas I had an interest in myself, Ideas I could understand. It was clear, that if I predicted what ideas others were going to invest in, I could make some profit speculating in that. However, I wanted the right ideas to come forward. It was a little like voting too, I voted for the ideas i liked. You know it was my chance to be heard, and I was honored.

K: OK, thanks, is there anything youʼd like to add about the idea exchange?

S: No, not really. It was fun and Iʼll be sure to participate in the future.

Interview from Case 3Employee in Pharmaceutical Co. Lisa, was active by sharing ideas, comments and investing in ideas.

K: Interviewer, KristofferL: Participant, Lisa

K: Thank you for participating in this interview first of all. If you could tell me about your introduction to the idea market?

L: Our first experience with the software was in conjunction with our theme-day. We went to a local hospital to see how our products were being used there, and we were given talks about challenges for the users and consumers of our product.

K: And that was almost all the employees at the company?

L: Basically, yes.

K: And you came back, and you were introduced to the idea market?

L: Yes, it was like a giant brainstorming for all of us.

K: ...and did it stay like a giant brainstorming or did it become more of a competition?

L: Well, I would have say a little of both. In one way, most of the people I talked to, were talking about who they were better than and so on. But especially on the top ideas, we kept sharing comments and input that would make sense for those ideas.

K: And it was your general impression, that this was how the idea market was to people.

L: Well, you know, it was a competition, right? And it only lasted a couple of weeks, so especially in the end people talked about their rankings. But especially the top two ideas were ones that I had heard about before, and people really talked about those. Needless to say, the men were really into checking their scores and talking about them!

K: As share prices in ideas went up and down, did you sell your idea-shares?

L: No actually I didn't . I didn't have the time. for me it wasn't the shareprices of the ideas, more the contents of the ideas. It was funny to see, a lot of the guys were really competitive, even though the rewards weren't amazing or anything. The girls, my friends at least were more concerned with

K: Did you win anything?

L: Actually, I think so, I was in 8th place, and the top 10 traders won some wine. So yes. We just haven't gotten the prizes yet.

K: But this wasn't too important for you?

L: No, actually I'd rather be able to give the money to charity. If that is possible.

K: A lot of people in the survey told me that they enjoyed taking part and that they would do it again. Tell me about your experience overall.

L: Yes I hope they do it again. It was fun. I think I checked my stocks a few times every day. And we had lots of fun just talking about the crazy ideas we should put in there!

K: Did it change anything in your day to day conversations with colleagues?

L: Not much, we always have a good tone amongst the group. I guess you could say, that an initiative like this fitted our culture quite well. Of course, we talk more about the next products and how our document processes could become better.

K: Anything bad you've encountered during the idea market participation?

L: Well, there is one thing. It all seemed a little unreal. The concept was so new to all of us. I think that if nothing manifests itself from this experience, if some of the ideas do not get turned into real things, it will stay unreal. So It's important that something comes from it.

K: Would you participate in a similar idea market again in the future?

L: Well, yes.

K: And if nothing came from this one?

L: Probably yes, because I had fun competing. But I'm quite sure that a lot of the people would be less likely to. They'd be disappointed, surely.

K: How does this compare to other employee innovation initiatives at the company?

L: Well, the only only we've had before was one where we had to come up with one idea per month. It was really fun to see how people would come up with ideas at the very last minute. This was better, because you could join on your own terms - If you just wanted to rate ideas, you could do that. If you wanted to comment, you limit yourself to doing that.

K: Thanks, is there anything you'd like to add, before ending the interview.

L: Just that it was great and that I'd try it again.

idea markets -in the innovation engine. APPENDIX

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Interview with participant behind the winning idea in case 2:The winning participant works in sales and marketing of the company, and has done so for roughly 12 years.

(Interview was carried out at the offices of the company in question in case 1. The interview was carried out in Danish, and transcribed in the same language. The passages that the writer has found to be of interest to the thesis have been translated)

(K) : Interviewer, Kristoffer(P) : Participant, Simon

K: First of all, Iʼd like to congratulate you on coming up with the winning idea, I also see you did quite well in the trader competition.

S: Yes, I think itʼs safe to say that I was quite involved in the competition.

K: First of all, tell me about how you perceived the whole thing in the beginning, when you where introduced to the competition. I mean the idea of subjecting your ideas to a system like this, a competition and a stock market?

S: Firstly, I was honored to be asked for my ideas. Iʼve worked here for more than 12 years, and Iʼve never been asked for my best ideas before.

K: And the stock market, the system?

S: It was funny. I have never traded stock in my whole life, so it was tricky to understand how the system worked in the beginning. But just as I was happy to be asked for my ideas, I was happy to look through my colleagues ideas, and isolate the best ones.

....It was really different, instead of just looking and saying, a thatʼs a good idea, you had tell youself: yes, but what is the best idea?

K: So did you follow the development of your own idea share?

S: Certainly, I mean you cannot spend too much time looking at the game, there is work to be done. But I did follow it. And my own profits from the shares i invested in. Sometimes I would check the market in the weekend. You know, it was fun.

...it was funny to see how people commented on my ideas, and people I didnʼt even know in Germany and Hong Kong wrote me to ask about my idea. As I said, it was a little honoring.

K: Did you leave any comments on the otherʼs ideas?

S: No actually I didnʼt. I spoke personally to one or two here in the offices about their ideas. that was it.

K: I noticed that you got about 15 comments on your own idea. You answered some of those?

S: Ah yes! People I didnʼt even know wrote to ask more specific questions and simply to cheer me on. I think it helped my idea along, maybe made it more specific and easier to understand. One or two comments were a little critical, but thatʼs what you get. The dialogue we had on the idea, for me was al little like marketing.

K: Alright, where did you get your idea from?

S: You know, Iʼve been here for so many years, and Iʼve discussed the need for this product with quite a few of our customers. But you know, normally new product come falling from the sky, and then you have to sell them. But this one has been in my head for quite some time.

K: How about comments? Did they improve the idea or were they more detrimental, putting it down?

S: I know that Iʼm not a scientist or in any way a technician. [.......]I decided that the idea might get even better and attain more investments, if I urged my colleagues to contribute. I Actually asked a lot of questions in the description.

K: Iʼve been told by the project manager that some users manipulated the system, in that they agreed to all invest in the idea that ended up in 4th spot. Can you tell me more about that?

S: True, I know a couple of those people. The idea that they put up was one that was tried 4 years ago, but got shut down. I think this was their window to show everyone that they still wanted to go ahead with the project, and they argument that with newer technology, it might be possible. I didnʼt invest, but it fine by me.

K: Tell me more about your strategy when it came to trading shares in your colleagues ideas.

S: I invested in ideas I had an interest in myself, Ideas I could understand. It was clear, that if I predicted what ideas others were going to invest in, I could make some profit speculating in that. However, I wanted the right ideas to come forward. It was a little like voting too, I voted for the ideas i liked. You know it was my chance to be heard, and I was honored.

K: OK, thanks, is there anything youʼd like to add about the idea exchange?

S: No, not really. It was fun and Iʼll be sure to participate in the future.

Interview from Case 3Employee in Pharmaceutical Co. Lisa, was active by sharing ideas, comments and investing in ideas.

K: Interviewer, KristofferL: Participant, Lisa

K: Thank you for participating in this interview first of all. If you could tell me about your introduction to the idea market?

L: Our first experience with the software was in conjunction with our theme-day. We went to a local hospital to see how our products were being used there, and we were given talks about challenges for the users and consumers of our product.

K: And that was almost all the employees at the company?

L: Basically, yes.

K: And you came back, and you were introduced to the idea market?

L: Yes, it was like a giant brainstorming for all of us.

K: ...and did it stay like a giant brainstorming or did it become more of a competition?

L: Well, I would have say a little of both. In one way, most of the people I talked to, were talking about who they were better than and so on. But especially on the top ideas, we kept sharing comments and input that would make sense for those ideas.

K: And it was your general impression, that this was how the idea market was to people.

L: Well, you know, it was a competition, right? And it only lasted a couple of weeks, so especially in the end people talked about their rankings. But especially the top two ideas were ones that I had heard about before, and people really talked about those. Needless to say, the men were really into checking their scores and talking about them!

K: As share prices in ideas went up and down, did you sell your idea-shares?

L: No actually I didn't . I didn't have the time. for me it wasn't the shareprices of the ideas, more the contents of the ideas. It was funny to see, a lot of the guys were really competitive, even though the rewards weren't amazing or anything. The girls, my friends at least were more concerned with

K: Did you win anything?

L: Actually, I think so, I was in 8th place, and the top 10 traders won some wine. So yes. We just haven't gotten the prizes yet.

K: But this wasn't too important for you?

L: No, actually I'd rather be able to give the money to charity. If that is possible.

K: A lot of people in the survey told me that they enjoyed taking part and that they would do it again. Tell me about your experience overall.

L: Yes I hope they do it again. It was fun. I think I checked my stocks a few times every day. And we had lots of fun just talking about the crazy ideas we should put in there!

K: Did it change anything in your day to day conversations with colleagues?

L: Not much, we always have a good tone amongst the group. I guess you could say, that an initiative like this fitted our culture quite well. Of course, we talk more about the next products and how our document processes could become better.

K: Anything bad you've encountered during the idea market participation?

L: Well, there is one thing. It all seemed a little unreal. The concept was so new to all of us. I think that if nothing manifests itself from this experience, if some of the ideas do not get turned into real things, it will stay unreal. So It's important that something comes from it.

K: Would you participate in a similar idea market again in the future?

L: Well, yes.

K: And if nothing came from this one?

L: Probably yes, because I had fun competing. But I'm quite sure that a lot of the people would be less likely to. They'd be disappointed, surely.

K: How does this compare to other employee innovation initiatives at the company?

L: Well, the only only we've had before was one where we had to come up with one idea per month. It was really fun to see how people would come up with ideas at the very last minute. This was better, because you could join on your own terms - If you just wanted to rate ideas, you could do that. If you wanted to comment, you limit yourself to doing that.

K: Thanks, is there anything you'd like to add, before ending the interview.

L: Just that it was great and that I'd try it again.

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Dav

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Furt

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Pre

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01Cu

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seque

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havio

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Lessi

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ech

risfm

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ld an

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rm

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ffee,

And

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06En

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rise 2

.0: T

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ombi

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qu

alita

tive a

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uant

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!!Int

erna

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sear

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ppro

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sA

rticl

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Patn

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ress

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tive I

ntell

igen

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log)

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mm

unity

-inte

llige

nce.c

om/b

logs

/pub

lic/2

006/

05/

form

s_of

_col

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htm

lBl

og

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trum

pf20

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istor

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ts: w

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esid

entia

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tions

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NC

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ctiv

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004

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tions

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rnat

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of C

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tic T

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id20

03T

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Org

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